1. The authors propose a new model for classifying the iris data set using a linear support vector machine (SVM) classifier with genetic algorithm optimization of the SVM's C and gamma parameters.
2. Principal component analysis was used to reduce the iris data set features from four to three before classification.
3. The genetic algorithm was shown to optimize the SVM parameters, achieving 98.7% accuracy on the iris data set classification compared to 95.3% accuracy without parameter optimization.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
EXTRACTING USEFUL RULES THROUGH IMPROVED DECISION TREE INDUCTION USING INFORM...ijistjournal
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchy’s knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.
EXTRACTING USEFUL RULES THROUGH IMPROVED DECISION TREE INDUCTION USING INFORM...ijistjournal
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchy’s knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
EXTRACTING USEFUL RULES THROUGH IMPROVED DECISION TREE INDUCTION USING INFORM...ijistjournal
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchy’s knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.
EXTRACTING USEFUL RULES THROUGH IMPROVED DECISION TREE INDUCTION USING INFORM...ijistjournal
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchy’s knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval IJECEIAES
Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm. This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-artworks.
CLASSIFICATION ALGORITHM USING RANDOM CONCEPT ON A VERY LARGE DATA SET: A SURVEYEditor IJMTER
Data mining environment produces a large amount of data, that need to be
analyses, pattern have to be extracted from that to gain knowledge. In this new period with
rumble of data both ordered and unordered, by using traditional databases and architectures, it
has become difficult to process, manage and analyses patterns. To gain knowledge about the
Big Data a proper architecture should be understood. Classification is an important data mining
technique with broad applications to classify the various kinds of data used in nearly every
field of our life. Classification is used to classify the item according to the features of the item
with respect to the predefined set of classes. This paper provides an inclusive survey of
different classification algorithms and put a light on various classification algorithms including
j48, C4.5, k-nearest neighbor classifier, Naive Bayes, SVM etc., using random concept.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as
automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic
clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization
stratagem. This evolutionary technique always aims
to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to
detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal
values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance
of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Hypothesis on Different Data Mining AlgorithmsIJERA Editor
In this paper, different classification algorithms for data mining are discussed. Data Mining is about
explaining the past & predicting the future by means of data analysis. Classification is a task of data mining,
which categories data based on numerical or categorical variables. To classify the data many algorithms are
proposed, out of them five algorithms are comparatively studied for data mining through classification. There are
four different classification approaches namely Frequency Table, Covariance Matrix, Similarity Functions &
Others. As work for research on classification methods, algorithms like Naive Bayesian, K Nearest Neighbors,
Decision Tree, Artificial Neural Network & Support Vector Machine are studied & examined using benchmark
datasets like Iris & Lung Cancer.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
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.
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.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
SVM-PSO based Feature Selection for Improving Medical Diagnosis Reliability u...cscpconf
Improving accuracy of supervised classification algorithms in biomedical applications,
especially CADx, is one of active area of research. This paper proposes construction of rotation
forest (RF) ensemble using 20 learners over two clinical datasets namely lymphography and
backache. We propose a new feature selection strategy based on support vector machines
optimized by particle swarm optimization for relevant and minimum feature subset for obtaining
higher accuracy of ensembles. We have quantitatively analyzed 20 base learners over two
datasets and carried out the experiments with 10 fold cross validation leave-one-out strategy
and the performance of 20 classifiers are evaluated using performance metrics namely accuracy
(acc), kappa value (K), root mean square error (RMSE) and area under receiver operating
characteristics curve (ROC). Base classifiers succeeded 79.96% & 81.71% average accuracies
for lymphography & backache datasets respectively. As for RF ensembles, they produced
average accuracies of 83.72% & 85.77% for respective diseases. The paper presents promising
results using RF ensembles and provides a new direction towards construction of reliable and robust medical diagnosis systems.
The effect of gamma value on support vector machine performance with differen...IJECEIAES
Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
Predicting the student performance is a great concern to the higher education managements.This
prediction helps to identify and to improve students' performance.Several factors may improve this
performance.In the present study, we employ the data mining processes, particularly classification, to
enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
On comprehensive analysis of learning algorithms on pedestrian detection usin...UniversitasGadjahMada
Despite the surge of deep learning, deploying the deep learning-based pedestrian detection into the real system faces hurdles, mainly due to the huge resource usages. The classical feature-based detection system still becomes feasible option. There have been many efforts to improve the performance of pedestrian detection system. Among many feature set, Histogram of Oriented Gradient seems to be very effective for person detection. In this research, various machine learning algorithms are investigated for person detection. Different machine learning algorithms are evaluated to obtain the optimal accuracy and speed of the system.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval IJECEIAES
Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm. This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-artworks.
CLASSIFICATION ALGORITHM USING RANDOM CONCEPT ON A VERY LARGE DATA SET: A SURVEYEditor IJMTER
Data mining environment produces a large amount of data, that need to be
analyses, pattern have to be extracted from that to gain knowledge. In this new period with
rumble of data both ordered and unordered, by using traditional databases and architectures, it
has become difficult to process, manage and analyses patterns. To gain knowledge about the
Big Data a proper architecture should be understood. Classification is an important data mining
technique with broad applications to classify the various kinds of data used in nearly every
field of our life. Classification is used to classify the item according to the features of the item
with respect to the predefined set of classes. This paper provides an inclusive survey of
different classification algorithms and put a light on various classification algorithms including
j48, C4.5, k-nearest neighbor classifier, Naive Bayes, SVM etc., using random concept.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as
automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic
clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization
stratagem. This evolutionary technique always aims
to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to
detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal
values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance
of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Hypothesis on Different Data Mining AlgorithmsIJERA Editor
In this paper, different classification algorithms for data mining are discussed. Data Mining is about
explaining the past & predicting the future by means of data analysis. Classification is a task of data mining,
which categories data based on numerical or categorical variables. To classify the data many algorithms are
proposed, out of them five algorithms are comparatively studied for data mining through classification. There are
four different classification approaches namely Frequency Table, Covariance Matrix, Similarity Functions &
Others. As work for research on classification methods, algorithms like Naive Bayesian, K Nearest Neighbors,
Decision Tree, Artificial Neural Network & Support Vector Machine are studied & examined using benchmark
datasets like Iris & Lung Cancer.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
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.
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.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
SVM-PSO based Feature Selection for Improving Medical Diagnosis Reliability u...cscpconf
Improving accuracy of supervised classification algorithms in biomedical applications,
especially CADx, is one of active area of research. This paper proposes construction of rotation
forest (RF) ensemble using 20 learners over two clinical datasets namely lymphography and
backache. We propose a new feature selection strategy based on support vector machines
optimized by particle swarm optimization for relevant and minimum feature subset for obtaining
higher accuracy of ensembles. We have quantitatively analyzed 20 base learners over two
datasets and carried out the experiments with 10 fold cross validation leave-one-out strategy
and the performance of 20 classifiers are evaluated using performance metrics namely accuracy
(acc), kappa value (K), root mean square error (RMSE) and area under receiver operating
characteristics curve (ROC). Base classifiers succeeded 79.96% & 81.71% average accuracies
for lymphography & backache datasets respectively. As for RF ensembles, they produced
average accuracies of 83.72% & 85.77% for respective diseases. The paper presents promising
results using RF ensembles and provides a new direction towards construction of reliable and robust medical diagnosis systems.
The effect of gamma value on support vector machine performance with differen...IJECEIAES
Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
Predicting the student performance is a great concern to the higher education managements.This
prediction helps to identify and to improve students' performance.Several factors may improve this
performance.In the present study, we employ the data mining processes, particularly classification, to
enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
On comprehensive analysis of learning algorithms on pedestrian detection usin...UniversitasGadjahMada
Despite the surge of deep learning, deploying the deep learning-based pedestrian detection into the real system faces hurdles, mainly due to the huge resource usages. The classical feature-based detection system still becomes feasible option. There have been many efforts to improve the performance of pedestrian detection system. Among many feature set, Histogram of Oriented Gradient seems to be very effective for person detection. In this research, various machine learning algorithms are investigated for person detection. Different machine learning algorithms are evaluated to obtain the optimal accuracy and speed of the system.
A novel ensemble modeling for intrusion detection system IJECEIAES
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs, and hence our system is robust and efficient.
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.
An overlapping conscious relief-based feature subset selection methodIJECEIAES
Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods.
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.
Support Vector Machine–Based Prediction System for a Football Match Resultiosrjce
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.
Clustering heterogeneous categorical data using enhanced mini batch K-means ...IJECEIAES
Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, k at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
is limited to a single server. As a solution for this, is the distributed digital artifact system, where resources are distributed among
different domains to enable inter-domain communication. Due to the emerging developments in web, attacks have increased rapidly,
among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
to an extent, by providing self reference and trusty URIs even when presented in a distributed environment. Any manipulation to the
data is efficiently identified and any further access to that data is blocked by informing user that the uniform location has been
changed. System uses self-reference to contain trusty URI for each resource, lineage algorithm for generating seed and SHA-512 hash
generation algorithm to ensure security. It is implemented on the semantic web, which is an extension to the world wide web, using
RDF (Resource Description Framework) to identify the resource. Hence the framework was developed to overcome existing
challenges by making the digital artifacts on the semantic web distributed to enable communication between different domains across
the network securely and thereby preventing MIMA.
For years, the Machine Learning community has focused on developing efficient
algorithms that can produce very accurate classifiers. However, it is often much easier
to find several good classifiers based on dataset combination, instead of single classifier
applied on deferent datasets. The advantages of using classifier dataset combinations
instead of a single one are twofold: it helps lowering the computational complexity by
using simpler models, and it can improve the classification accuracy and performance.
Most Data mining applications are based on pattern matching algorithms, thus improving
the performance of the classification has a positive impact on the quality of the overall
data mining task. Since combination strategies proved very useful in improving the
performance, these techniques have become very important in applications such as
Cancer detection, Speech Technology and Natural Language Processing .The aim of this
paper is basically to propose proprietary metric, Normalized Geometric Index (NGI)
based on the latent properties of datasets for improving the accuracy of data mining tasks.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
essential element in current competitive markets. Security and comfort are the main criteria and parameters of selecting a car.
Therefore, standard dataset of CAR involving six features and characteristics and 1728 instances have been used. In this paper, it
has been tried to select a car with the best characteristics by using intelligent algorithms (Random Forest, J48, SVM,
NaiveBayse) and combining these algorithms with aggregated classifiers such as Bagging and AdaBoostMI. In this study, speed
and accuracy of intelligent algorithms in identifying the best car have been taken into account.
Classification of data is a data mining technique based on machine learning is used to classification of each item set in as a set of dataset into a set of predefined labelled as classes or groups. Classification is tasks for different application such as text classification, image classification, class’s predictions, data Classification etc. In this paper, we presenting the major classification techniques used for prediction of classes using supervised learning dataset. Several major types of classification method including Random Forest, Naive Bayes, Support Vector Machine (SVM) techniques. The goal of this review paper is to provide a review, accuracy and comparative between different classification techniques in data mining.
Novel Ensemble Tree for Fast Prediction on Data StreamsIJERA Editor
Data Streams are sequential set of data records. When data appears at highest speed and constantly, so predicting
the class accordingly to the time is very essential. Currently Ensemble modeling techniques are growing
speedily in Classification of Data Stream. Ensemble learning will be accepted since its benefit to manage huge
amount of data stream, means it will manage the data in a large size and also it will be able to manage concept
drifting. Prior learning, mostly focused on accuracy of ensemble model, prediction efficiency has not considered
much since existing ensemble model predicts in linear time, which is enough for small applications and
accessible models workings on integrating some of the classifier. Although real time application has huge
amount of data stream so we required base classifier to recognize dissimilar model and make a high grade
ensemble model. To fix these challenges we developed Ensemble tree which is height balanced tree indexing
structure of base classifier for quick prediction on data streams by ensemble modeling techniques. Ensemble
Tree manages ensembles as geodatabases and it utilizes R tree similar to structure to achieve sub linear time
complexity
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCERcscpconf
In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer
Similar to A new model for iris data set classification based on linear support vector machine parameter's optimization (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
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• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 1079 - 1084
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from indecent parameter settings, while the perfect categorization accuracy of SVM stems from seeking
optimal parameters.
a. The authors submitted a new manner which optimize SVM's parameters effectively and reduce the time
of optimization and calculations cost using tow nested real valued genetic algorithm (NRGA).
The NRGA compared to the conventional optimization mechanisms which operate on seeking the whole
parameters together [7].
b. A notation was submitted in [8] for determining SVM parameters depending on minds from design of
experiences, which initiated with an extremely rough grid comprising the complete search range and
repetitive revised both the grid resolution and search boarders, safeguarding the number of forms at each
phase almost constants.
c. Genetic algorithm (GA) is trend to be completely pretty at finding in general perfect universal solutions.
GA has been vastly adopted for parameter setting. In [9] a manner based on GA was suggested to
simultaneously optimize SVM 'S parameters and attribute subset. In [10] GA is fused with asymptotic
attitudes of SVM which then guides the search to the right line of perfect generalization error in the super
parameter space.
d. This study [9] develops a novel manner termed PSO+SVM. PSO based approach for parameter
determining and feature selection, and then a comparison is conducted of gained results with other
approaches. The SVM+PSO gained a better accuracy of classification than other tests.
2. CLASSIFIERS
Classification is imperative for data mining. The learning algorithm [11] establishes a classifier in
a given set of measurement, for instance, a set of characteristic data (x1, x2,…., xn), where xi denotes feature
data Xi. The purpose of classification is to initiate the actuality of groups when given a set of observation
(unsupervised learning) or where various categories prevail and the target is classified into one of
the previous categories (supervised learning) [12]. Supervised learning has been employed in this study as
the classification method.
2.1. SVM
In this part, we focus SVM, a manner using for a classification the linear and nonlinear data.
The SVM algorithm operates as follows: the nonlinear mapping is used to convert the training data into
a higher distance, under the fresh distance; it investigates for the linear perfect segregating hyperplane
(i.e., a “decision boundary” segregating the tuples of one class from another). With a convenient nonlinear
mapping to an adequately elevated distance, the data of two classes can be always segregated by
a hyperplane. The SVM finds this hyperplane using support vectors (“essential” training tuples) and edges
(defined by the support vectors) [13, 14].
2.2. Genetic algorithm (GA)
Genetic algorithms (GA) operate with a collection of nominee solutions named a population.
Depending on the Darwinian principle of „existence of the fittest‟, the GA earns the perfect solution after
sequences of reduplicate calculations. GA products consecutive populations of alternate solutions that is
representative by a chromosome, i.e. a solution to the problem, till acceptable results are earned. GA
a general adaptive optimization search methodology based on a direct analogy to Darwinian natural selection
and genetics in biological systems is a promising alternative to conventional heuristic methods. In this study,
we essentially utilize GA to refine the parameters (C and γ) of the SVM model for iris dataset [15, 16]. GA as
a wrapper method combined with PCA as filter method and tested using SVM to classification leaves [16].
The results showed that GA combined with SVM given computing time effectively and improve accuracy.
GA also used to select important features and instances then tested using SVM and k-nearest neighbors
(KNN) [17-19]. Gain Ratio (filter) combined with sequential forward selection (SFS) wrapper proposed to
deal with three datasets; iris, breast, and dermatology [20, 21]. A various feature selection methods also
compared, they were information gain, gain ratio (GR), symmetrical uncertainty (SU), Chi square (CS),
relief, and correlation based feature selection (CFS) [19]. The result showed that CFS was the most stable
with the highest accuracy for handling data with two classes.
3. METHOD
As mentioned before SVM classifier was built to classify iris dataset into different classes.
The using of GA is to optimize SVM's parameters (c, gamma), in order to obtain higher and best
accuracy [22]. The iris dataset has four attributes, principle components analysis (PCA) algorithm was
3. Int J Elec & Comp Eng ISSN: 2088-8708
A new model for iris data set classification based on linear support vector machine … (Zahraa Faiz Hussain)
1081
applied to reduce these features (feature reduction), and then only three features were chooses. Whereas
principal component analysis (PCA) is a mathematical execution that converts a number of (possibly)
correlated variables into a (smaller) number of uncorrelated variables named principal components. PCA is
a dimension-decreasing instrument that can be utilized to drooping a great set of inconstant to a little set that
stay involves most of the information in the big set [12, 2].
The presented technique in this study used the IRIS dataset acquired from the UCI Machine
Learning Repository. The dataset is in a multivariate group as it provides the statistic on the Iris plant type
based on four characteristics which include width, width and petal - length, sepal - length, and values as
presented in Figure 1. The dataset is composed of three groups with 50 cases each and a total of 50 cases.
The dataset were first processed by removing missing data values. The type of Iris plant is the forecasted
characteristic in this dataset [5].
Figure 1. IRIS dataset
Step-by-step of new model in this research for iris data set classification based on linear support
vector machine parameter's optimization is:
Step-1: The Iris dataset in CSV is computed as the input.
Step-2: Divide the data into test and training datasets. In this study, the dataset was partitioned into 70%
training and 30% testing.
Step-3: Distinguish the training dataset based on the class values, that is, 1, 2 and 3.
Step-4: Determine the standard deviation and mean values for the individual data case based on the class
values.
Step-5: Choose the SVM (C and γ) parameters as input to genetic algorithm optimization.
Step-6: Apply the optimal value of the (C and γ) parameters as an initial value to the process of classification
using SVM.
Step-7: Utilize the model and generate predictions.
Step-8: Determine the prediction accuracy through the comparison of the class data of test dataset.
This accuracy is evaluated depending on the ratio between 0 to 100%.
4. RESULTS AND CORRELATIONS
The suggested model presented in Section 4 was performed on the Iris dataset with and without
Step-5. In each run, the obtained results were evaluated based on the accuracy of the SVM classifier.
The obtained results showed that the accuracy of the SVM increased to 98.7 using Step-5 and about 95.3%
without Step-5. All the results, with the optimization, are presented in Figures 2, 3, 4, 5, 6 and 7, respectively.
The results of proposed method show the powerful of using genetic algorithm to optimize the (C and gamma)
parameter of SVM classifier.
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 1079 - 1084
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Figure 2. The scatter plot without genetic
Figure 3. The ROC curve without genetic Figure 4. The confusion matrix without genetic
Figure 5. The scatter plot with genetic
5. Int J Elec & Comp Eng ISSN: 2088-8708
A new model for iris data set classification based on linear support vector machine … (Zahraa Faiz Hussain)
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Figure 6. The ROC curve with genetic Figure 7. The confusion matrix with genetic
5. CONCLUSIONS AND RECOMMENDATION
This paper have proposed a newly mode for classifying iris data set using SVM classifier and
genetic algorithm, in addition PCA algorithm was use for features reduction. This proposed mode is to
optimize c and gamma parameters of linear SVM. As shown above the results obtained from applied GA on
iris dataset is 98.7 and without GA is 97.78. GA was used to optimize SVM's parameters (c, gamma),
in order to promotion an efficacious SVM model with high accuracy and stability, the optimal parameter seek
on SVM plays a fateful role. Inadvisable parameter settings result in inferior classification performance.
For the future work, this study can be extend into two part; firstly by improving the performance of GA such
as hybrid GA with other method as works done by [22-24], and secondly by apply feature selection method
in SVM for optimal parameter setting as proposed in [25].
REFERENCES
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Material Fatigue Life Prediction," School of Mechanical Engineering, Shanghai University of Engineering Science,
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[2] X. Z. Li and J M. Kong, "Application of GA–SVM method with parameter optimization forlandslide development
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[4] Abbas F. H. Alharan, Hayder K. Fatlawi, Nabeel Salih Ali, “A cluster-based feature selection method for image
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pp1433-1442, June 2019.
[5] Maryam, N. AkhmadSetiawan, and O. Wahyunggoro., "A Hybrid Feature Selection Method Using Multiclass SVM
for Diagnosis of Erythemato-Squamous Disease," International Conference on Mathematics World
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[6] L. Talavera., "An evaluation of filter and wrapper methods for feature selection in categorical clustering,"
Adv. Intell. Data Anal. VI, pp. 742, 2005.
[7] P. Liao, X. Zhang, and K. Li., "Parameter Optimization for Support Vector Machine Based on Nested Genetic
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[8] C. Staelin., Parameter selection for support vector machines, Technical Report HPL-2002-354 (R.1), HP
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[9] C. L. Huang and C. J. Wang, "A GA-based feature selection and parameters optimization for support vector
machine," Expert Systems with Applications, vol. 31(2), pp. 231-240, 2006.
[10] C. H. Wu, G. H. Tzeng, Y. J. Goo, and W. C. Fang., "A real-valued genetic algorithm to optimize the parameters
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[11] S. W. Lin, K. C. Ying, S. C. Chen, and Z. J. Lee., "Particle swarm optimization for parameter determination and
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[12] Min, J. H. and Lee, Y. C. "Bankruptcy prediction using support vector machine with optimal choice of kernel
function parameters," Expert Syst. Appl., vol. 28, pp. 603-614, 2005.
[13] Gu, J. Y., Liu, J. F, and Chen, M., "A modified regression prediction algorithm of large sample data based on
SVM," Computer Engineering, vol. 40(1), pp. 161-166, 2014.
[14] M. Z. A. Ahmed Hussein Ali, "A Survey on Vertical and Horizontal Scaling Platforms for Big Data Analytics,"
International Journal of Integrated Engineering, 2018.
[15] M. Z. A. Ahmed Hussein Ali, "An Efficient Model for Data Classification Based on SVM Grid Parameter
Optimization and PSO Feature Weight Selection," International Journal of Integrated Engineering, 2018.
[16] C. Tsai, W. Eberle, and C. Chu., "Knowledge-Based Systems Genetic algorithms in feature and instance selection,"
Knowledge-Based Syst., vol. 39, pp. 240-247, 2013.
[17] M. Karabatak and M. C. Ince, "A new feature selection method based on association rules for diagnosis of
erythemato-squamous diseases," Expert Syst. Appl., vol. 36(10), pp. 12500-12505, 2009.
[18] D. Zhang, et al, "A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration
Prediction," BioMed Research International, 2015.
[19] Kasim S., Hassan R., Mohd N. S., Ramlan R., Mahdin H, and Fudzee M. F. M., "A Comparative Study of Different
Template Matching Techniques for Twin Iris Recognition," International Journal on Advanced Science,
Engineering and Information Technology, vol. 7(4-2), pp. 1581-1588, 2017.
[20] Kasim S., Hassan R., Zaini N. H., Syifaa‟ Ahmad A., Ramli A. A, and Saedudin R. R., "A Study on Facial
Expression Recognition Using Local Binary Pattern," International Journal on Advanced Science, Engineering and
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[21] Zin N.A.M., Asmuni H., Hamed H.N.A., Othman R.M., Kasim S., Hassan R., Zakaria Z, and Roslan R., "Contact
lens classification by using segmented lens boundary features," Indonesian Journal of Electrical Engineering and
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[22] Ismail M.A., Razak K.A.B., Moorthy K., Mezhuyev V., Kasim S, and Ibrahim A.O., "Newton Competitive Genetic
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[23] Ismail M.A., Mezhuyev V., Deris S., Mohamad, M.S., Kasim S, and Saedudin R.R., "Multi-objective Optimization
of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method,"
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[24] Ismail M.A., Mezhuyev V. Moorthy K., Kasim S, and Ibrahim A.O., "Optimisation of Biochemical Systems
Production using Hybrid of Newton method, Differential Evolution Algorithm and Cooperative Coevolution
Algorithm," Indonesian Journal of Electrical Engineering and Computer Science, vol. 8(1), pp. 27-35, 2017.
[25] Ibrahim A., Hussien W., Yagoop A, and Ismail M., "Feature Selection and Radial Basis Function Network for
Parkinson Disease Classification," Kurdistan Journal of Applied Research, vol. 2(3), pp. 167-171, 2017.
![International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 1, February 2020, pp. 1079~1084
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i1.pp1079-1084 1079
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
A new model for iris data set classification based on linear
support vector machine parameter's optimization
Zahraa Faiz Hussain1
, Hind Raad Ibraheem2
, Mohammad Alsajri3
, Ahmed Hussein Ali4
,
Mohd Arfian Ismail5
, Shahreen Kasim6
, Tole Sutikno7
1,2,3,4
Computer Science Department, AL Salam University College, Iraq
3,4
Department of Computer Science, College of Education, Al-Iraqia University, Iraq
5
Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Malaysia
6
Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia
7
Department of Electrical and Computer Engineering, Universitas Ahmad Dahlan, Indonesia
Article Info ABSTRACT
Article history:
Received Apr 9, 2019
Revised Sep 29, 2019
Accepted Oct 10, 2019
Data mining is known as the process of detection concerning patterns from
essential amounts of data. As a process of knowledge discovery.
Classification is a data analysis that extracts a model which describes
an important data classes. One of the outstanding classifications methods in
data mining is support vector machine classification (SVM). It is capable of
envisaging results and mostly effective than other classification methods.
The SVM is a one technique of machine learning techniques that is well
known technique, learning with supervised and have been applied perfectly
to a vary problems of: regression, classification, and clustering in diverse
domains such as gene expression, web text mining. In this study,
we proposed a newly mode for classifying iris data set using SVM classifier
and genetic algorithm to optimize c and gamma parameters of linear SVM,
in addition principle components analysis (PCA) algorithm was use for
features reduction.
Keywords:
Classification
Data mining
Genetic algorithm
Iris dataset
Parameter optimization
SVM
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Mohammad Alsajri,
Computer Science Department,
AL Salam University College,
BAGHDAD - Sidiyah: Near Al Waleed Center, Iraq.
Email: mohammad.cs88@gmail.com
1. INTRODUCTION
Classification is a manner of data analysis which used to elicit a classifier to classify important data
classes. These classifiers can expect categorical data (detached, unordered) class label [1]. Also classification
is an important field in the data mining and machine learning by concluding obscure classes of samples using
the learning of renowned classes of samples [2-3]. As an example, rating bank loan application can be
classified as safety or risky after a construction of classification model. This analysis supplied us with a better
comprehension of the data at big magnitude. Many classification approaches have been suggested in machine
learning, pattern recognition, and statistics. Classification can be achieved in a process of two-steps.
The construction of a classification based on preceding data is achieved in the first stage. In the second stage,
specifies if the accuracy of a model is admissible, and if so, we utilize the model to classify fresh data [4].
Support vector machine (SVM) Classifier is a renowned classification method employed for predicting
the results of datasets [5]. The proposed model was assessed on an IRIS dataset gained from the UCI
Machine Learning Database [6].
The creation of SVM model with high prediction accuracy and consistency is based on seeking
the ideal parameters on SVM, since it plays an essential role. Weakness classification performance result](https://image.slidesharecdn.com/v5310oct29sep9apr21125editamir-201203065652/85/A-new-model-for-iris-data-set-classification-based-on-linear-support-vector-machine-parameter-s-optimization-1-320.jpg)
![ ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 1079 - 1084
1080
from indecent parameter settings, while the perfect categorization accuracy of SVM stems from seeking
optimal parameters.
a. The authors submitted a new manner which optimize SVM's parameters effectively and reduce the time
of optimization and calculations cost using tow nested real valued genetic algorithm (NRGA).
The NRGA compared to the conventional optimization mechanisms which operate on seeking the whole
parameters together [7].
b. A notation was submitted in [8] for determining SVM parameters depending on minds from design of
experiences, which initiated with an extremely rough grid comprising the complete search range and
repetitive revised both the grid resolution and search boarders, safeguarding the number of forms at each
phase almost constants.
c. Genetic algorithm (GA) is trend to be completely pretty at finding in general perfect universal solutions.
GA has been vastly adopted for parameter setting. In [9] a manner based on GA was suggested to
simultaneously optimize SVM 'S parameters and attribute subset. In [10] GA is fused with asymptotic
attitudes of SVM which then guides the search to the right line of perfect generalization error in the super
parameter space.
d. This study [9] develops a novel manner termed PSO+SVM. PSO based approach for parameter
determining and feature selection, and then a comparison is conducted of gained results with other
approaches. The SVM+PSO gained a better accuracy of classification than other tests.
2. CLASSIFIERS
Classification is imperative for data mining. The learning algorithm [11] establishes a classifier in
a given set of measurement, for instance, a set of characteristic data (x1, x2,…., xn), where xi denotes feature
data Xi. The purpose of classification is to initiate the actuality of groups when given a set of observation
(unsupervised learning) or where various categories prevail and the target is classified into one of
the previous categories (supervised learning) [12]. Supervised learning has been employed in this study as
the classification method.
2.1. SVM
In this part, we focus SVM, a manner using for a classification the linear and nonlinear data.
The SVM algorithm operates as follows: the nonlinear mapping is used to convert the training data into
a higher distance, under the fresh distance; it investigates for the linear perfect segregating hyperplane
(i.e., a “decision boundary” segregating the tuples of one class from another). With a convenient nonlinear
mapping to an adequately elevated distance, the data of two classes can be always segregated by
a hyperplane. The SVM finds this hyperplane using support vectors (“essential” training tuples) and edges
(defined by the support vectors) [13, 14].
2.2. Genetic algorithm (GA)
Genetic algorithms (GA) operate with a collection of nominee solutions named a population.
Depending on the Darwinian principle of „existence of the fittest‟, the GA earns the perfect solution after
sequences of reduplicate calculations. GA products consecutive populations of alternate solutions that is
representative by a chromosome, i.e. a solution to the problem, till acceptable results are earned. GA
a general adaptive optimization search methodology based on a direct analogy to Darwinian natural selection
and genetics in biological systems is a promising alternative to conventional heuristic methods. In this study,
we essentially utilize GA to refine the parameters (C and γ) of the SVM model for iris dataset [15, 16]. GA as
a wrapper method combined with PCA as filter method and tested using SVM to classification leaves [16].
The results showed that GA combined with SVM given computing time effectively and improve accuracy.
GA also used to select important features and instances then tested using SVM and k-nearest neighbors
(KNN) [17-19]. Gain Ratio (filter) combined with sequential forward selection (SFS) wrapper proposed to
deal with three datasets; iris, breast, and dermatology [20, 21]. A various feature selection methods also
compared, they were information gain, gain ratio (GR), symmetrical uncertainty (SU), Chi square (CS),
relief, and correlation based feature selection (CFS) [19]. The result showed that CFS was the most stable
with the highest accuracy for handling data with two classes.
3. METHOD
As mentioned before SVM classifier was built to classify iris dataset into different classes.
The using of GA is to optimize SVM's parameters (c, gamma), in order to obtain higher and best
accuracy [22]. The iris dataset has four attributes, principle components analysis (PCA) algorithm was](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)