System identification (SI) is a method of determining a mathematical model
for a system given a set of input-output data. A representation is made using
a mathematical model based on certain specified assumptions. In SI, model
structure selection is a step where a model structure perceived as an adequate
system representation is selected. A typical rule is that the final model must
have a good balance between parsimony and accuracy. As a popular search
method, genetic algorithm (GA) is used for selecting a model structure.
However, the optimality of the final model depends much on the
effectiveness of GA operators. This paper presents a mating technique
named single parent mating (SPM) in GA for use in a real robotic SI. This
technique is based on the chromosome structure of the parents such that a
single parent is sufficient in achieving mating that eases the search for the
optimal model. The results show that using three different objective
functions (Akaike information criterion, Bayesian information criterion and
parameter magnitude–based information criterion 2) respectively, GA with
the mating technique is able to find more optimal models than without the
mating technique. Validations show that the selected models using the
mating technique are acceptable.
A chi-square-SVM based pedagogical rule extraction method for microarray data...IJAAS Team
Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration
and exploitation, a hybrid model with a popular meta-heuristic algorithm named
spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset
from dataset by removing the noise and outliers prior to feeding the data to the
wrapper SMO. To enhance the quality of the solution, simulated annealing is
deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SMO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.
Enhancing feature selection with a novel hybrid approach incorporating geneti...IJECEIAES
Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters
especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic
algorithm.
Genetic algorithm started to be applied in information retrieval system in order to optimize the query by
genetic algorithm, a good query is a set of terms that express accurately the information need while being
usable within collection corpus, the last part of this specification is critical for the matching process to be
efficient, that is why most research efforts are actually put toward the query improvement.
We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean
model, and Language model in information retrieval (IR), the algorithm used crossover and mutation
operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those,
and remain unchanged during execution. GA is developed to support adaptive adjustment of mutation and
crossover probability; this allows faster attainment of better solutions. The paper has been tested using
242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.
Comparison of Cell formation techniques in Cellular manufacturing using three...IJERA Editor
In the present era of globalization and competitive market, cellular manufacturing has become a vital tool for
meeting the challenges of improving productivity, which is the way to sustain growth. Getting best results of
cellular manufacturing depends on the formation of the machine cells and part families. This paper examines
advantages of ART method of cell formation over array based clustering algorithms, namely ROC-2 and DCA.
The comparison and evaluation of the cell formation methods has been carried out in the study. The most
appropriate approach is selected and used to form the cellular manufacturing system. The comparison and
evaluation is done on the basis of performance measure as grouping efficiency and improvements over the
existing cellular manufacturing system is presented.
Comparison of Cell formation techniques in Cellular manufacturing using three...IJERA Editor
In the present era of globalization and competitive market, cellular manufacturing has become a vital tool for
meeting the challenges of improving productivity, which is the way to sustain growth. Getting best results of
cellular manufacturing depends on the formation of the machine cells and part families. This paper examines
advantages of ART method of cell formation over array based clustering algorithms, namely ROC-2 and DCA.
The comparison and evaluation of the cell formation methods has been carried out in the study. The most
appropriate approach is selected and used to form the cellular manufacturing system. The comparison and
evaluation is done on the basis of performance measure as grouping efficiency and improvements over the
existing cellular manufacturing system is presented.
A chi-square-SVM based pedagogical rule extraction method for microarray data...IJAAS Team
Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration
and exploitation, a hybrid model with a popular meta-heuristic algorithm named
spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset
from dataset by removing the noise and outliers prior to feeding the data to the
wrapper SMO. To enhance the quality of the solution, simulated annealing is
deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SMO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.
Enhancing feature selection with a novel hybrid approach incorporating geneti...IJECEIAES
Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters
especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic
algorithm.
Genetic algorithm started to be applied in information retrieval system in order to optimize the query by
genetic algorithm, a good query is a set of terms that express accurately the information need while being
usable within collection corpus, the last part of this specification is critical for the matching process to be
efficient, that is why most research efforts are actually put toward the query improvement.
We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean
model, and Language model in information retrieval (IR), the algorithm used crossover and mutation
operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those,
and remain unchanged during execution. GA is developed to support adaptive adjustment of mutation and
crossover probability; this allows faster attainment of better solutions. The paper has been tested using
242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.
Comparison of Cell formation techniques in Cellular manufacturing using three...IJERA Editor
In the present era of globalization and competitive market, cellular manufacturing has become a vital tool for
meeting the challenges of improving productivity, which is the way to sustain growth. Getting best results of
cellular manufacturing depends on the formation of the machine cells and part families. This paper examines
advantages of ART method of cell formation over array based clustering algorithms, namely ROC-2 and DCA.
The comparison and evaluation of the cell formation methods has been carried out in the study. The most
appropriate approach is selected and used to form the cellular manufacturing system. The comparison and
evaluation is done on the basis of performance measure as grouping efficiency and improvements over the
existing cellular manufacturing system is presented.
Comparison of Cell formation techniques in Cellular manufacturing using three...IJERA Editor
In the present era of globalization and competitive market, cellular manufacturing has become a vital tool for
meeting the challenges of improving productivity, which is the way to sustain growth. Getting best results of
cellular manufacturing depends on the formation of the machine cells and part families. This paper examines
advantages of ART method of cell formation over array based clustering algorithms, namely ROC-2 and DCA.
The comparison and evaluation of the cell formation methods has been carried out in the study. The most
appropriate approach is selected and used to form the cellular manufacturing system. The comparison and
evaluation is done on the basis of performance measure as grouping efficiency and improvements over the
existing cellular manufacturing system is presented.
APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL IJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on
mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in
1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used
cosine similarity and jaccards to compute similarity between the query and documents, and used two
proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at
evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study
concluded that we might have several improvements when using adaptive genetic algorithms.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
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.
ON THE PREDICTION ACCURACIES OF THREE MOST KNOWN REGULARIZERS : RIDGE REGRESS...ijaia
The work in this paper shows intensive empirical experiments using 13 datasets to understand the regularization effectiveness of ridge regression, the lasso estimate, and elastic net regularization methods. The study offers a deep understanding of how the datasets affect the goodness of the prediction accuracy of each regularization method for a given problem given the diversity in the datasets used. The results have shown that datasets play crucial rules on the performance of the regularization method and that the
predication accuracy depends heavily on the nature of the sampled datasets.
A Novel Hybrid Voter Using Genetic Algorithm and Performance HistoryWaqas Tariq
Triple Modular Redundancy (TMR) is generally used to increase the reliability of real time systems where three similar modules are used in parallel and the final output is arrived at using voting methods. Numerous majority voting techniques have been proposed in literature however their performances are compromised for some typical set of module output value. Here we propose a new voting scheme for analog systems retaining the advantages of previous reported schemes and reduce the disadvantages associated with them. The scheme utilizes a genetic algorithm and previous performances history of the modules to calculate the final output. The scheme has been simulated using MATLAB and the performance of the voter has been compared with that of fuzzy voter proposed by Shabgahi et al [4]. The performance of the voter proposed here is better than the existing voters.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
An Heterogeneous Population-Based Genetic Algorithm for Data Clusteringijeei-iaes
As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
Null-values imputation using different modification random forest algorithmIAESIJAI
Today, the world lives in the era of information and data. Therefore, it has become vital to collect and keep them in a database to perform a set of processes and obtain essential details. The null value problem will appear through these processes, which significantly influences the behaviour of processes such as analysis and prediction and gives inaccurate outcomes. In this concern, the authors decide to utilise the random forest technique by modifying it to calculate the null values from datasets got from the University of California Irvine (UCL) machine learning repository. The
database of this scenario consists of connectionist bench, phishing websites, breast cancer, ionosphere, and COVID-19. The modified random forest algorithm is based on three matters and three number of null values. The samples chosen are founded on the proposed less redundancy bootstrap. Each tree has distinctive features depending on hybrid features selection. The final effect is considered based on ranked voting for classification. This scenario found that the modified random forest algorithm executed more suitable accuracy results than the traditional algorithm as it relied on four parameters and got sufficient accuracy in imputing the null value, which is grown by 9.5%, 6.5%, and 5.25% of one, two and three null values in the
same row of datasets, respectively.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL IJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on
mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in
1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used
cosine similarity and jaccards to compute similarity between the query and documents, and used two
proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at
evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study
concluded that we might have several improvements when using adaptive genetic algorithms.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
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.
ON THE PREDICTION ACCURACIES OF THREE MOST KNOWN REGULARIZERS : RIDGE REGRESS...ijaia
The work in this paper shows intensive empirical experiments using 13 datasets to understand the regularization effectiveness of ridge regression, the lasso estimate, and elastic net regularization methods. The study offers a deep understanding of how the datasets affect the goodness of the prediction accuracy of each regularization method for a given problem given the diversity in the datasets used. The results have shown that datasets play crucial rules on the performance of the regularization method and that the
predication accuracy depends heavily on the nature of the sampled datasets.
A Novel Hybrid Voter Using Genetic Algorithm and Performance HistoryWaqas Tariq
Triple Modular Redundancy (TMR) is generally used to increase the reliability of real time systems where three similar modules are used in parallel and the final output is arrived at using voting methods. Numerous majority voting techniques have been proposed in literature however their performances are compromised for some typical set of module output value. Here we propose a new voting scheme for analog systems retaining the advantages of previous reported schemes and reduce the disadvantages associated with them. The scheme utilizes a genetic algorithm and previous performances history of the modules to calculate the final output. The scheme has been simulated using MATLAB and the performance of the voter has been compared with that of fuzzy voter proposed by Shabgahi et al [4]. The performance of the voter proposed here is better than the existing voters.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
An Heterogeneous Population-Based Genetic Algorithm for Data Clusteringijeei-iaes
As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
Null-values imputation using different modification random forest algorithmIAESIJAI
Today, the world lives in the era of information and data. Therefore, it has become vital to collect and keep them in a database to perform a set of processes and obtain essential details. The null value problem will appear through these processes, which significantly influences the behaviour of processes such as analysis and prediction and gives inaccurate outcomes. In this concern, the authors decide to utilise the random forest technique by modifying it to calculate the null values from datasets got from the University of California Irvine (UCL) machine learning repository. The
database of this scenario consists of connectionist bench, phishing websites, breast cancer, ionosphere, and COVID-19. The modified random forest algorithm is based on three matters and three number of null values. The samples chosen are founded on the proposed less redundancy bootstrap. Each tree has distinctive features depending on hybrid features selection. The final effect is considered based on ranked voting for classification. This scenario found that the modified random forest algorithm executed more suitable accuracy results than the traditional algorithm as it relied on four parameters and got sufficient accuracy in imputing the null value, which is grown by 9.5%, 6.5%, and 5.25% of one, two and three null values in the
same row of datasets, respectively.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Similar to Single parent mating in genetic algorithm for real robotic system identification (20)
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
K-centroid convergence clustering identification in one-label per type for di...IAESIJAI
Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3 I system with the most important features and remain the same accuracy.
Plant leaf detection through machine learning based image classification appr...IAESIJAI
Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the enhanced k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
Backbone search for object detection for applications in intrusion warning sy...IAESIJAI
In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Multi-channel microseismic signals classification with convolutional neural n...IAESIJAI
Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Sophisticated face mask dataset: a novel dataset for effective coronavirus di...IAESIJAI
Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Deep neural network for lateral control of self-driving cars in urban environ...IAESIJAI
The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray i...IAESIJAI
Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions. Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
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This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
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The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
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- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Single parent mating in genetic algorithm for real robotic system identification
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 1, March 2023, pp. 201~208
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i1.pp201-208 201
Journal homepage: http://ijai.iaescore.com
Single parent mating in genetic algorithm for real robotic
system identification
Md Fahmi Abd Samad1,2
, Farah Ayiesya Zainuddin1
1
Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
2
Center for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
Article Info ABSTRACT
Article history:
Received Oct 2, 2021
Revised Jul 14, 2022
Accepted Aug 12, 2022
System identification (SI) is a method of determining a mathematical model
for a system given a set of input-output data. A representation is made using
a mathematical model based on certain specified assumptions. In SI, model
structure selection is a step where a model structure perceived as an adequate
system representation is selected. A typical rule is that the final model must
have a good balance between parsimony and accuracy. As a popular search
method, genetic algorithm (GA) is used for selecting a model structure.
However, the optimality of the final model depends much on the
effectiveness of GA operators. This paper presents a mating technique
named single parent mating (SPM) in GA for use in a real robotic SI. This
technique is based on the chromosome structure of the parents such that a
single parent is sufficient in achieving mating that eases the search for the
optimal model. The results show that using three different objective
functions (Akaike information criterion, Bayesian information criterion and
parameter magnitude–based information criterion 2) respectively, GA with
the mating technique is able to find more optimal models than without the
mating technique. Validations show that the selected models using the
mating technique are acceptable.
Keywords:
Discrete-time system
Evolutionary computation
Genetic algorithm
Mathematical modelling
Robotic system
System identification
This is an open access article under the CC BY-SA license.
Corresponding Author:
Md Fahmi Abd Samad
Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Email: mdfahmi@utem.edu.my
1. INTRODUCTION
System identification (SI) is known as a field of study where an optimal mathematical model,
relating the variables and terms of a system, is determined. This is done by using the input-output data from
the system. By developing the model, better control of the system may be achieved [1], [2]. There are two
types of system modelling i.e. continuous-time and discrete-time modelling. Noting that data acquisition in
industry or laboratory is made by instants of time, discrete-time modelling is commonly seen. There are 4
main steps in SI and these are data acquisition, model structure selection, parameter estimation and, lastly,
model validation. The description of an optimal model is one that has adequate accuracy in predicting the
response of the system but at the same time, is parsimonious in form. A parsimonious model structure, that
contain fewer variables and/or terms, is desirable because the analysis and control of the system becomes
easier [3].
To identify an optimal model for a system within a short time and cost, high efficiency in modelling
is needed. Therefore, researchers have turned to meta-heuristic methods, including evolutionary computation
as a method to allow optimal search for the system’s model [4]–[8]. Evolutionary computation, more
specifically genetic algorithm (GA), has proven its strength and endurance, and able to reduce computational
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burden. GA has become an interesting area of investigation among researchers for many applications such as
wireless sensor network energy optimization [9], control of vehicles [10], [11], modelling of disease severity
[12], scheduling in medical field [13], aeronautics and robotics [14].
Although GA has been adopted in SI, its efficiency is still lacking which can be observed from long
processing time, premature convergence or non-optimal setting of GA operators that causes loss of
computation time and restricted global performance. Premature convergence to local optima is one of the
most frequent difficulties that arise when applying GA to complex problem. It occurs when genetic operators
can no longer generate offsprings that are fitter than their suboptimal parents. Premature convergence is
associated with the loss of diversity in the population. However, too much population diversity can lead to a
dramatic deterioration of GA efficiency [15].
Aside from the common operators of GA selection, crossover and mutation and although, relatively
rare, researchers have started looking into mating after selection to enhance GA. An example include the
introduction of a self-adaptive mating based on parent similarity or fitness [15]. Another example introduces
gender and kinship to the individuals in GA [16]. The idea is also adopted in [17] and used together with tabu
search in [18].
With proper mating technique, the “marriage” of the parents must be able to explore new search
space of solution, producing more varied offsprings (hence, in the context of SI, may mean system model)
that cannot be achieved by common procedure of GA. This paper aims at introducing and implementing a
new mating technique, named single parent mating (SPM), which is simpler yet effective, for a real robotic
SI. The technique is shown to be capable of exploring new search space of solutions, thus producing more
optimal and valid models according to three different objective functions (OF). The sections are divided as:
section 2 explains the method of study including the mechanism of the mating proposal, section 3 reveals the
results along with validation analysis and section 4 concludes the findings of the paper.
2. RESEARCH METHOD
2.1. GA with mating technique
GA is known as an optimization method that takes the metaphor of species evolution [19]. In
traditional GA, the search for an optimal model is made through three important processes: selection,
crossover and mutation [20], [21]. The selection process copies good chromosomes into a mating pool - some
may be copied a number of times. Common crossover operates by the mating of two or more parents taken
randomly from the mating pool once the selection process is completed. However, there is a possibility that
the 2 selected parents have the same features causing the process to produce similar offsprings to parents and
thus suppressing the evolution.
In this paper, to speed up the search and avoid premature convergence in the population, the
algorithm incorporates a mating technique named SPM. The idea is to have 2 parents of completely different
characteristics to be mated in order to try out new offsprings for the next generation. In SPM, once the
parents are transferred to the mating pool after the selection process, they are copied and inverted to form a
new set of parents. Using a binary representation, all bits 1 are changed to 0 and vice versa. The mating is
achieved by pairing the original parent with its inverted self, hence the name SPM. The mating emphasizes
how the pair of chromosomes is made, not how their informations are exchanged, as information exchange is
carried out through crossover. Figure 1 illustrates that there are 24 possible offsprings with SPM when
applied together with uniform crossover, as illustration. By this way, bigger search space is explored,
diversity of the population is maintained and higher variability of offsprings is accomplished in the next
generation. Figure 2 shows the procedure of GA using the mating technique.
2.2. Real robotic system
The real data comes from a flexible robot arm system available from [22]. The flexible robot arm
has 1,024 data points of an input (measured reaction torque of the robotic structure) and an output (the
acceleration of the flexible arm). Markovsky et al. [23], [24] selected lag 4 for a linear model identification
of the system. Yassin et al. [25], it is identified that the system’s suitable specification is maximum order of
input lag=2 and maximum order of output lag=4, also within a linear form.
2.3. Simulation setup
In this study, the chromosomes in GA represent specific models for SI. The models, in the form of
nonlinear autoregressive with exogenous input (NARX) models, are represented using binary representation
where 1 is presence of term and 0 for omission of term. Adopting some literature results, the specification of
the search space is as: maximum nonlinearity=2, maximum order of output lag=4, maximum order of input
lag=4 and time delay=1. This specification makes up 44 terms to be selected for model structure and the
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number of possible models to choose from is 244
-1>1013
. Once a model structure is selected, the parameters
are estimated using the least squares method. Then, they are evaluated based on the minimization of a
specific optimality measurement a.k.a. OF in identifying the system. Three different types of OF i.e. Akaike
information criteria (AIC), Bayesian information criteria (BIC) and parameter magnitude-based information
criteria 2 (PMIC2) are used to evaluate the models throughout evolution. AIC and BIC may be referred from
e.g. [26] while PMIC2 is demonstrated in [27] and [28].
The specification of GA is as: population size=200 and 100, maximum generation=300, bit-flip
mutation probability=0.01 and single-point crossover probability=0.6. The study uses roulette-wheel
selection and the elitism strategy. In the elitism strategy, the chromosome that is evaluated as the best is
brought forward, unchanged, into the population of the next iteration (generation). The processes are repeated
until termination (maximum generation).
Prior study conducted using the mating technique with single-point crossover on simulated data sets
revealed that the method found more optimal models by setting its implementation in the range of 10% to
20% of population, inclusively. Hence, in this study, GA is carried out using two specific percentages of the
parents in the mating pool where the mating pool is the same size as the population. The percentages tested
were 0% and 15%. As an example, for a 15% SPM, 15% of the parents in the mating pool are copied and
inverted. This makes another set of parents of size 15% of population. Mating was done between the parents
and its inverted selves (making up 30% of a population). The remaining 70% comes from the initial mating
pool. With this setting, it may be noted that 0% represents GA without mating. For each percentage, 15 runs
of GA on the real robot arm system data set are made.
Figure 1. Possible offsprings with single parent mating (SPM) Figure 2. GA with SPM
3. RESULTS AND DISCUSSION
3.1. Model structure selection
Finding an optimal model, that has adequate accuracy of prediction yet parsimonious in its structure,
is aimed through the minimization of the OF. Figure 3 shows the average OF value of the best chromosome
versus generation when using 0% SPM technique (that represents no mating and labelled “0” in legend) and
15% SPM technique (labelled “0.15”) for the real flexible robot arm data with 200 population size. Based on
Figure 3(a), the graph of 15% SPM started at a high value but then decreased rapidly until the 100th
generation when using AIC. It then settled rather consistently until the 300th
generation. The decrement when
using 0% SPM was rather slow until the maximum generation. The OF value of 15% SPM is better (lower)
than 0% SPM throughout the evolution. Closer investigation revealed that GA with 15% SPM managed to
find the same, presumably, the most optimal model in all its runs while with 0% SPM, the very same model
is found in only 5 runs. The model has 8 regressors with an OF of -10745. Some of the models in 0% SPM
gives higher OF of -10741. The best model from 15% SPM is written as:
𝑦(𝑡) = 3.158𝑦(𝑡 − 1) − 4.439𝑦(𝑡 − 2) + 3.13𝑦(𝑡 − 3) − 0.982𝑦(𝑡 − 4)
+0.019𝑢(𝑡 − 1) − 0.029𝑢(𝑡 − 2) + 0.055𝑢(𝑡 − 3) − 0.030𝑢(𝑡 − 4) (1)
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Figures 3(b) to 3(c) show that the results using BIC and PMIC2 have quite the same pattern as graph
in Figure 3(a). The graphs show that 15% SPM is better than 0% SPM, noted by achieving lower OF average
of the best chromosomes, and, additionally, by shorter time. Similar to the observation when using AIC, with
BIC, GA with 15% SPM found the same most optimal model (according to BIC) in all runs with an OF of
-10707 whilst 0% SPM found it in 8 runs. Some of the models in 0% SPM gives higher OF of -10694. The
model found with BIC has 7 regressors and is:
𝑦(𝑡) = 3.128𝑦(𝑡 − 1) − 4.367𝑦(𝑡 − 2) + 3.058𝑦(𝑡 − 3) − 0.952𝑦(𝑡 − 4)
+0.004𝑢(𝑡 − 1) + 0.026𝑢(𝑡 − 3) − 0.015𝑢(𝑡 − 4) (2)
Selecting these linear models as the optimal ones within the space of nonlinear model choices agree
with [25]. When using PMIC2, models with lower OF value is found in 4 runs with 15% SPM than the
lowest of 0% SPM. The best model of PMIC2 contains 29 regressors. It contains several nonlinear terms and
the linear ones are 𝑦(𝑡 − 2), 𝑦(𝑡 − 3) and 𝑦(𝑡 − 4) with an OF value of 7.032, compared to 21.692 - the
lowest when using 0% SPM.
(a) (b)
(c)
Figure 3. Best chromosome’s OF value for robot arm with 200 population size (a) AIC model, (b) BIC
model, and (c) PMIC2 model
Next, another test is made by reducing the population size to 100. This affects GA in a way that less
parents are inverted, making the effectiveness of SPM questionable. Figure 4 shows the average results.
Based on Figure 4(a), it is seen again that, throughout evolution, the OF of the best model in 15% SPM is
lower than that of 0% SPM. The decrement of 15% SPM is rapid until it found a model with a very low OF
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whilst the decrement of 0% SPM is slow. 5% SPM found the same model as with 200 population size (1) in
all its runs while 0% SPM found the very same, presumably, the most optimal model in 10 runs.
Figures 4(b) to 4(c) show the OF average of the best chromosomes when using BIC and PMIC2,
respectively. The pattern with PMIC2 is not exactly the same as with AIC and BIC but the outcome is the
same. In all the OFs, 15% SPM found models with lower OF than 0% SPM throughout evolutions. With
BIC, 0% SPM found, presumably, the most optimal model in 7 runs whilst 15% SPM found the same model
in all its runs. This is the same model when using 200 population size (2). When using PMIC2, 8 runs in 15%
SPM ended with final models of lower OF than the best model found using 0% SPM in all its runs. The best
model contains several nonlinear terms and the linear ones are 𝑦(𝑡 − 1) and 𝑦(𝑡 − 3). This model has a
higher OF than the one with 200 population size i.e. 15.583.
All the results obtained with 200 and 100 population size indicate that the population in 15% SPM
contain more genetic diversity, allowing more optimal models to be found quicker. It indicates that bigger
search space was explored and higher variety of offsprings was found in the next generation, compared to no
mating. When a population is unable to produce offsprings of higher variability than their parents, an
algorithm becomes trapped in local optima. This may have caused 0% SPM to be stuck with the near-
optimals.
(a) (b)
(c)
Figure 4. Best chromosome’s OF value for robot arm with 100 population size (a) AIC model, (b) BIC
model, and (c) PMIC2 model
3.2. Model validation
To complement the whole procedure of SI, correlation tests are used as validation stage to ensure
that no other significant terms and/or variables are omitted from the model [29]. Only the models selected
using AIC and BIC are discussed since the validation of the PMIC2 models do not provide significant result.
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Since the selected models using AIC and BIC are of linear forms, Figure 5 shows the linear correlation tests,
consisting of error autocorrelation test (𝜙𝜀𝜀) and input-error cross correlation test (𝜙𝑢𝜀), carried out onto the
results of the models, respectively. The dotted horizontal lines resemble the bandwidth such that a valid
model with 95% confidence should have the lines within the bandwidth. They both look the same and it can
be seen that some points lie outside of the bandwidth. The data reveals that there are small differences
between Figures 5(a) and 5(b) where the biggest difference in 𝜙𝜀𝜀 test is at lag -16 while in 𝜙𝑢𝜀 test, the
biggest difference is at lag -1. Nonetheless, the result is similar to the ones found in [30] where the authors
commented that it is difficult to achieve perfect results for real life cases, and that sufficiently good, as can be
seen, is acceptable. These validation deficiencies may be inherent from wrong selection of lag orders or
nonlinearity. Including the noise terms will improve the model as carried out in [25].
(a) (b)
Figure 5. Linear correlation tests for best model in 15% SPM (a) AIC model (1) and (b) BIC model (2)
Cross validation is also carried out on the results, as shown in Figure 6 and Figure 7, where the
predicted output is superimposed to the real output and the error (difference of value between the two)
plotted. With the range of real output data from -0.7883 to 0.7891, the highest error is 0.0147 whilst the
average error is 0.0043 in Figure 6. From Figure 7, the highest error is 0.0151 whilst the average error is
0.0042. These errors are small and they show that the models selected by GA using AIC and BIC
incorporating the mating technique are valid and acceptable. Furthermore, the mean square error of the
models are 2.620×10-5
and 2.632×10-5
for AIC and BIC models, respectively. These data are comparable to
the ones from [30] who obtained 2.69×10-5
and 2.72×10-5
for training data set and testing data set,
respectively.
Figure 6. Superimposition of predicted output of
model using AIC with 15% SPM onto real output
Figure 7. Superimposition of predicted output of
model using AIC with 15% SPM onto real output
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4. CONCLUSION
The aim of this paper is to show the effectiveness of SPM for GA in optimization of a real discrete-
time SI. Three types of OFs are used and according to all OFs, GA with SPM is able to find more optimal
models, and additionally, quicker than without mating. In all runs, the models found using the mating
technique have either lower or equal OF value than the runs without mating, indicating that the selected
models are parsimonious yet with adequate accuracy. In validating the models, correlation tests are done to
the selected models. Although the correlation tests show that the models do not fulfill the 95% confidence
bandwidth completely, which is expected to be due to inherent noise of real data, superimposition of the
predicted output from the models with the real output value provide convincing validation. Even when using
a small population size, the technique is shown to be capable of reaching into untested territory of the search
space. Using the mating technique, new points are able to be explored thus enabling the search for optimal
models to become more promising than the traditional procedure of GA. From application perspective, future
work may focus on changing the phenotype-to-genotype conversion for a more effective search of optimal
models while from the method’s perspective, other crossover and selection types may be tested together with
SPM to see whether faster convergence may be achieved.
ACKNOWLEDGEMENTS
The authors would like to express gratitude to Ministry of Higher Education Malaysia and Universiti
Teknikal Malaysia Melaka (UTeM) for their financial support through FRGS/1/2018/TK03/UTEM/02/13
grant, technical support and facility.
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BIOGRAPHIES OF AUTHORS
Md Fahmi Abd Samad has earned his Ph.D. degree in mechanical engineering
from Universiti Teknologi Malaysia and currently serving as an associate professor in Faculty
of Mechanical Engineering, Universiti Teknikal Malaysia Melaka. His research interests are in
system identification, control engineering and evolutionary computation. He is also a
Chartered Engineer. He can be contacted at email: mdfahmi@utem.edu.my.
Farah Ayiesya Zainuddin has earned Bachelor of Mechanical Engineering
(Thermal-Fluids) and Master of Science in Mechanical Engineering from Universiti Teknikal
Malaysia Melaka. Her research areas include genetic algorithm and aeronautics. She is
currently pursuing PhD degree in mechanical engineering. She can be contacted at email:
p041910001@student.utem.edu.my.