This document describes a web-based decision support system (DSS) for solving linear programming problems. The DSS incorporates two linear programming algorithms (revised simplex algorithm and exterior primal simplex algorithm), as well as ten scaling techniques, five basis update methods, and eight pivoting rules. Users can select different combinations of these algorithms and methods to solve their linear programming problems within the DSS. The DSS was designed and implemented using MATLAB and Java to provide a web interface for decision makers to access and utilize its linear programming solving capabilities.
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques ijsc
Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
This document summarizes an empirical study comparing several supervised machine learning approaches for word sense disambiguation: Naive Bayes, decision tree, decision list, and support vector machine (SVM). The study used a dataset of 15 words annotated with senses from WordNet and Senseval-3. Each approach was implemented and evaluated based on its accuracy in identifying the correct sense of each word. The results showed that the decision list approach achieved the highest overall accuracy of 69.12%, followed by SVM at 56.11%, naive Bayes at 58.32%, and decision tree at 45.14%. Thus, the study concluded that decision list performed best on this dataset for the task of word sense disambiguation.
An overlapping conscious relief-based feature subset selection methodIJECEIAES
Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
A BI-OBJECTIVE MODEL FOR SVM WITH AN INTERACTIVE PROCEDURE TO IDENTIFY THE BE...gerogepatton
A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques ijsc
Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
This document summarizes an empirical study comparing several supervised machine learning approaches for word sense disambiguation: Naive Bayes, decision tree, decision list, and support vector machine (SVM). The study used a dataset of 15 words annotated with senses from WordNet and Senseval-3. Each approach was implemented and evaluated based on its accuracy in identifying the correct sense of each word. The results showed that the decision list approach achieved the highest overall accuracy of 69.12%, followed by SVM at 56.11%, naive Bayes at 58.32%, and decision tree at 45.14%. Thus, the study concluded that decision list performed best on this dataset for the task of word sense disambiguation.
An overlapping conscious relief-based feature subset selection methodIJECEIAES
Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
A BI-OBJECTIVE MODEL FOR SVM WITH AN INTERACTIVE PROCEDURE TO IDENTIFY THE BE...gerogepatton
A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
A BI-OBJECTIVE MODEL FOR SVM WITH AN INTERACTIVE PROCEDURE TO IDENTIFY THE BE...ijaia
A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
GRID COMPUTING: STRATEGIC DECISION MAKING IN RESOURCE SELECTIONIJCSEA Journal
The rapid development of computer networks around the world generated new areas especially in computer instruction processing. In grid computing, instruction processing is performed by external processors available to the system. An important topic in this area is task scheduling to available external resources. However, we do not deal with this topic here. In this paper we intend to work on strategic decision making on selecting the best alternative resources for processing instructions with respect to criteria in special conditions. Where the criteria might be security, political, technical, cost, etc. Grid computing should be determined with respect to the processing objectives of instructions of a program. This paper seeks a way through combining Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help us in ranking and selecting available resources according to considerable criteria in allocating instructions to resources. Therefore, our findings will help technical managers of organizations in choosing as well as ranking candidate alternatives for processing program instructions.
This document discusses using particle swarm optimization based on variable neighborhood search (PSO-VNS) to attack classical cryptography ciphers. PSO is a population-based optimization algorithm inspired by bird flocking behavior. VNS is a metaheuristic algorithm that explores neighborhoods of solutions to escape local optima. The paper proposes improving PSO with VNS to find better solutions. It evaluates PSO-VNS on substitution and transposition ciphers, finding it recovers keys better than standard PSO and other variants.
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
This document introduces an R package called PSF that implements a Pattern Sequence based Forecasting (PSF) algorithm for univariate time series forecasting. The PSF algorithm clusters time series data and then predicts future values based on identifying repeating patterns of clusters. The PSF package contains functions that perform the main steps of the PSF algorithm, including selecting the optimal number of clusters, selecting the optimal window size, and making predictions for a given window size and number of clusters. The package aims to promote and simplify the use of the PSF algorithm for time series forecasting.
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.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
In the present day huge amount of data is generated in every minute and transferred frequently. Although
the data is sometimes static but most commonly it is dynamic and transactional. New data that is being
generated is getting constantly added to the old/existing data. To discover the knowledge from this
incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time
consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary.
The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The
paper proposes a dimension reduction algorithm that can be applied in dynamic environment for
generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the
reduct and build up the corresponding classification system. The method analyzes the new dataset, when it
becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data
set, interesting optimal classification rule sets are generated. The concepts of discernibility relation,
attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of
dynamic reduct set, and optimal classification rules are selected using PSO method, which not only
reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed
method has been applied on some benchmark dataset collected from the UCI repository and dynamic
reduct is computed, and from the reduct optimal classification rules are also generated. Experimental
result shows the efficiency of the proposed method.
The document presents a pre-trained image processing transformer (IPT) model that can be applied to multiple low-level computer vision tasks like denoising, super-resolution, and deraining. The IPT model consists of multiple task-specific heads and tails connected to a shared transformer body. The model is trained on over 10 million image pairs generated from the ImageNet dataset by adding synthetic noise and corruption. The input images are encoded into patches and processed by the transformer's encoder-decoder architecture using position and task embeddings. Experimental results show the pre-trained IPT model outperforms state-of-the-art methods on various benchmarks when fine-tuned for specific tasks.
Particle Swarm Optimization in the fine-tuning of Fuzzy Software Cost Estimat...Waqas Tariq
Software cost estimation deals with the financial and strategic planning of software projects. Controlling the expensive investment of software development effectively is of paramount importance. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage. More recently, attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Fuzzy logic is one such technique which can cope with uncertainties. In the present paper, Particle Swarm Optimization Algorithm (PSOA) is presented to fine tune the fuzzy estimate for the development of software projects . The efficacy of the developed models is tested on 10 NASA software projects, 18 NASA projects and COCOMO 81 database on the basis of various criterion for assessment of software cost estimation models. Comparison of all the models is done and it is found that the developed models provide better estimation
A Fuzzy Interactive BI-objective Model for SVM to Identify the Best Compromis...ijfls
This document summarizes a research paper that proposes a fuzzy bi-objective support vector machine (SVM) model to identify infected COVID-19 patients. The model uses SVM classification with two objectives - maximizing margin between classes and minimizing misclassification errors. An α-cut transforms the fuzzy model into a classical bi-objective problem solved using weighting methods. This generates multiple efficient solutions. An interactive process then identifies the best compromise based on minimizing the number of support vectors in each class. The model constructs a utility function to measure COVID-19 infection levels based on the SVM classification.
A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMIS...ijfls
A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).
This document summarizes an international journal article that proposes a two-phase algorithm for face recognition in the frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The algorithm works in two phases: the first phase uses Euclidean distance to determine the K nearest neighbor training samples of a test sample. The second phase represents the test sample as a linear combination of the K nearest neighbors and classifies the sample based on which class representation has the smallest deviation from the test sample. Experimental results on FERET and ORL face databases show the two-phase algorithm based on DCT and DFT outperforms other methods like two-phase sparse representation and PCA/LDA in terms of classification accuracy.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Comparison between the genetic algorithms optimization and particle swarm opt...IAEME Publication
The document compares the genetic algorithms optimization and particle swarm optimization methods for designing close range photogrammetry networks. It presents the genetic algorithm and particle swarm optimization as two popular meta-heuristic algorithms inspired by natural evolution and collective animal behavior, respectively. The document develops mathematical models representing the genetic algorithm and particle swarm optimization for close range photogrammetry network design and evaluates them in a test field to reinforce the theoretical aspects.
Multi criteria decision making (MCDM) techniques in today’s organizations, as a key
to performance measurement comes more to the foreground with the advancement in the high
technology. During recent years, many studies have been conducted to obtain a ranking
among many alternatives via measuring performance of each of them against many criteria.
Managerial decision making problems like supplier selection, weapon selection, project
selection, site selection etc are dealt with many multi criteria decision making methods like
TOPSIS, AHP-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution),
PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation),
ELECTRE, VIKOR etc in crisp throughout the literature. In this work, we first compare
several MCDM methodologies to validate the consistency of them on a standard dataset of
plant layout problem. We proposed M-TOPSIS, A-TOPSIS procedure to select a suitable
layout for the comparative study. Results of M-TOPSIS and A-TOPSIS have been employed
to build an unsupervised artificial neural network (ANN) to obtain a new ranking of
alternatives. This study proposes an approach of deriving the rank value, in order to get
optimal configuration, from the average of more than one set of rank results obtained through
the deployment of MCDM methodologies
Meta heuristic based clustering of two-dimensional data using-2IAEME Publication
This document proposes two new metaheuristic clustering algorithms called HDMNS and HMDMNS that are hybrid versions of the neighborhood search algorithm. HDMNS uses data mining techniques like frequent itemset mining once to find an improved solution after the initial solution from neighborhood search. HMDMNS applies data mining techniques like frequent itemset mining multiple times iteratively to find the optimal solution. Experimental results on clustering two-dimensional data show that both HDMNS and HMDMNS outperform the traditional k-means clustering algorithm in terms of cluster quality, with HMDMNS performing the best. Execution times are also compared, showing HMDMNS can be used as an efficient clustering algorithm.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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A BI-OBJECTIVE MODEL FOR SVM WITH AN INTERACTIVE PROCEDURE TO IDENTIFY THE BE...ijaia
A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
GRID COMPUTING: STRATEGIC DECISION MAKING IN RESOURCE SELECTIONIJCSEA Journal
The rapid development of computer networks around the world generated new areas especially in computer instruction processing. In grid computing, instruction processing is performed by external processors available to the system. An important topic in this area is task scheduling to available external resources. However, we do not deal with this topic here. In this paper we intend to work on strategic decision making on selecting the best alternative resources for processing instructions with respect to criteria in special conditions. Where the criteria might be security, political, technical, cost, etc. Grid computing should be determined with respect to the processing objectives of instructions of a program. This paper seeks a way through combining Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help us in ranking and selecting available resources according to considerable criteria in allocating instructions to resources. Therefore, our findings will help technical managers of organizations in choosing as well as ranking candidate alternatives for processing program instructions.
This document discusses using particle swarm optimization based on variable neighborhood search (PSO-VNS) to attack classical cryptography ciphers. PSO is a population-based optimization algorithm inspired by bird flocking behavior. VNS is a metaheuristic algorithm that explores neighborhoods of solutions to escape local optima. The paper proposes improving PSO with VNS to find better solutions. It evaluates PSO-VNS on substitution and transposition ciphers, finding it recovers keys better than standard PSO and other variants.
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
This document introduces an R package called PSF that implements a Pattern Sequence based Forecasting (PSF) algorithm for univariate time series forecasting. The PSF algorithm clusters time series data and then predicts future values based on identifying repeating patterns of clusters. The PSF package contains functions that perform the main steps of the PSF algorithm, including selecting the optimal number of clusters, selecting the optimal window size, and making predictions for a given window size and number of clusters. The package aims to promote and simplify the use of the PSF algorithm for time series forecasting.
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.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
In the present day huge amount of data is generated in every minute and transferred frequently. Although
the data is sometimes static but most commonly it is dynamic and transactional. New data that is being
generated is getting constantly added to the old/existing data. To discover the knowledge from this
incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time
consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary.
The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The
paper proposes a dimension reduction algorithm that can be applied in dynamic environment for
generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the
reduct and build up the corresponding classification system. The method analyzes the new dataset, when it
becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data
set, interesting optimal classification rule sets are generated. The concepts of discernibility relation,
attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of
dynamic reduct set, and optimal classification rules are selected using PSO method, which not only
reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed
method has been applied on some benchmark dataset collected from the UCI repository and dynamic
reduct is computed, and from the reduct optimal classification rules are also generated. Experimental
result shows the efficiency of the proposed method.
The document presents a pre-trained image processing transformer (IPT) model that can be applied to multiple low-level computer vision tasks like denoising, super-resolution, and deraining. The IPT model consists of multiple task-specific heads and tails connected to a shared transformer body. The model is trained on over 10 million image pairs generated from the ImageNet dataset by adding synthetic noise and corruption. The input images are encoded into patches and processed by the transformer's encoder-decoder architecture using position and task embeddings. Experimental results show the pre-trained IPT model outperforms state-of-the-art methods on various benchmarks when fine-tuned for specific tasks.
Particle Swarm Optimization in the fine-tuning of Fuzzy Software Cost Estimat...Waqas Tariq
Software cost estimation deals with the financial and strategic planning of software projects. Controlling the expensive investment of software development effectively is of paramount importance. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage. More recently, attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Fuzzy logic is one such technique which can cope with uncertainties. In the present paper, Particle Swarm Optimization Algorithm (PSOA) is presented to fine tune the fuzzy estimate for the development of software projects . The efficacy of the developed models is tested on 10 NASA software projects, 18 NASA projects and COCOMO 81 database on the basis of various criterion for assessment of software cost estimation models. Comparison of all the models is done and it is found that the developed models provide better estimation
A Fuzzy Interactive BI-objective Model for SVM to Identify the Best Compromis...ijfls
This document summarizes a research paper that proposes a fuzzy bi-objective support vector machine (SVM) model to identify infected COVID-19 patients. The model uses SVM classification with two objectives - maximizing margin between classes and minimizing misclassification errors. An α-cut transforms the fuzzy model into a classical bi-objective problem solved using weighting methods. This generates multiple efficient solutions. An interactive process then identifies the best compromise based on minimizing the number of support vectors in each class. The model constructs a utility function to measure COVID-19 infection levels based on the SVM classification.
A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMIS...ijfls
A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).
This document summarizes an international journal article that proposes a two-phase algorithm for face recognition in the frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The algorithm works in two phases: the first phase uses Euclidean distance to determine the K nearest neighbor training samples of a test sample. The second phase represents the test sample as a linear combination of the K nearest neighbors and classifies the sample based on which class representation has the smallest deviation from the test sample. Experimental results on FERET and ORL face databases show the two-phase algorithm based on DCT and DFT outperforms other methods like two-phase sparse representation and PCA/LDA in terms of classification accuracy.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Comparison between the genetic algorithms optimization and particle swarm opt...IAEME Publication
The document compares the genetic algorithms optimization and particle swarm optimization methods for designing close range photogrammetry networks. It presents the genetic algorithm and particle swarm optimization as two popular meta-heuristic algorithms inspired by natural evolution and collective animal behavior, respectively. The document develops mathematical models representing the genetic algorithm and particle swarm optimization for close range photogrammetry network design and evaluates them in a test field to reinforce the theoretical aspects.
Multi criteria decision making (MCDM) techniques in today’s organizations, as a key
to performance measurement comes more to the foreground with the advancement in the high
technology. During recent years, many studies have been conducted to obtain a ranking
among many alternatives via measuring performance of each of them against many criteria.
Managerial decision making problems like supplier selection, weapon selection, project
selection, site selection etc are dealt with many multi criteria decision making methods like
TOPSIS, AHP-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution),
PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation),
ELECTRE, VIKOR etc in crisp throughout the literature. In this work, we first compare
several MCDM methodologies to validate the consistency of them on a standard dataset of
plant layout problem. We proposed M-TOPSIS, A-TOPSIS procedure to select a suitable
layout for the comparative study. Results of M-TOPSIS and A-TOPSIS have been employed
to build an unsupervised artificial neural network (ANN) to obtain a new ranking of
alternatives. This study proposes an approach of deriving the rank value, in order to get
optimal configuration, from the average of more than one set of rank results obtained through
the deployment of MCDM methodologies
Meta heuristic based clustering of two-dimensional data using-2IAEME Publication
This document proposes two new metaheuristic clustering algorithms called HDMNS and HMDMNS that are hybrid versions of the neighborhood search algorithm. HDMNS uses data mining techniques like frequent itemset mining once to find an improved solution after the initial solution from neighborhood search. HMDMNS applies data mining techniques like frequent itemset mining multiple times iteratively to find the optimal solution. Experimental results on clustering two-dimensional data show that both HDMNS and HMDMNS outperform the traditional k-means clustering algorithm in terms of cluster quality, with HMDMNS performing the best. Execution times are also compared, showing HMDMNS can be used as an efficient clustering algorithm.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
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Healing can occur in two ways: Regeneration and Repair
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Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
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