The document describes a hybrid algorithm that combines a modified multiple operations using statistical tests (MMOST) constructive algorithm with an improved teaching-learning based optimization (ITLBO) algorithm for neural network training. The hybrid algorithm simultaneously optimizes the neural network structure and weights. The MMOST algorithm constructs different network structures, while the ITLBO algorithm finds the optimal weights for each structure. The hybrid algorithm, called MCO-ITLBO, is tested on classification and time series prediction problems and is shown to outperform other algorithms in terms of error rates and network complexity. Experimental results demonstrate that the MCO-ITLBO algorithm provides better performance than algorithms using only constructive or training methods.
Fuzzy clustering has been widely studied and applied in a variety of key areas of science and
engineering. In this paper the Improved Teaching Learning Based Optimization (ITLBO)
algorithm is used for data clustering, in which the objects in the same cluster are similar. This
algorithm has been tested on several datasets and compared with some other popular algorithm
in clustering. Results have been shown that the proposed method improves the output of
clustering and can be efficiently used for fuzzy clustering.
This document discusses online feature selection (OFS) for data mining applications. It addresses two tasks of OFS: 1) learning with full input, where the learner can access all features to select a subset, and 2) learning with partial input, where only a limited number of features can be accessed for each instance. Novel algorithms are presented for each task, and their performance is analyzed theoretically. Experiments on real-world datasets demonstrate the efficacy of the proposed OFS techniques for applications in computer vision, bioinformatics, and other domains involving high-dimensional sequential data.
Neighborhood search methods with moth optimization algorithm as a wrapper met...IJECEIAES
Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flame Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Iaetsd an enhanced feature selection forIaetsd Iaetsd
The document discusses feature selection techniques for machine learning applications. It proposes an Enhanced Fast Clustering-based Feature Selection (EFAST) algorithm. The EFAST algorithm works in two steps: 1) features are clustered using graph-theoretic clustering methods, and 2) the most relevant representative feature strongly correlated with the target categories is selected from each cluster to form the optimal feature subset. Features from different clusters are relatively independent, so EFAST has a high chance of selecting a set of useful and independent features. The algorithm was tested on real-world data and showed improved performance over other feature selection methods by reducing features while also improving classifier performance.
Investigations on Hybrid Learning in ANFISIJERA Editor
Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN) is a network of interconnected artificial processing elements (called neurons) that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS), which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM) to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN) and RKLM can combine the advantages of two systems and avoid their disadvantages.
Soft Computing based Learning for Cognitive Radioidescitation
Over the last decade the world of wireless communications has been undergoing
some crucial changes, which have brought it at the forefront of international research and
development interest, eventually resulting in the advent of a multitude of innovative
technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh
networks and Software Defined Radio. Such a highly varying radio environment calls for
intelligent management, allocation and usage of a scarce resource, namely the radio
spectrum. One of the most prominent emerging technologies that promise to handle such
situations is Cognitive Radio. Cognitive Radio systems are based on Software Defined Radio
technology and utilize intelligent software packages that enrich their transceivers with the
highly attractive properties of self-awareness, adaptability and capability to learn. The
Cognitive Engine, the intelligent system behind the Cognitive Radio, combines sensing,
learning, and optimization algorithms to control and adapt the radio system from the
physical layer and up the communication stack. The integration of a learning engine can be
very important for improving the stability and reliability of the discovery and evaluation of
the configuration capabilities. To this effect, many different learning techniques are
available and can be used by a Cognitive Radio ranging from pure lookup tables to
arbitrary combinations of soft Computing techniques, which include among others:
Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy
systems, Hidden Markov Models, etc. The proposed work contributes in this direction,
aiming to develop a learning scheme and work towards solving problems related to learning
phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the
performance assessment work, conducted in order to design and use an appropriate
structure, while indicative results need to be presented and discussed in order to showcase
the benefits of incorporating such learning schemes into Cognitive Radio systems.
Subsequently feasibility of such learning schemes could be tested with simulations. In the
near future, such learning schemes are expected to assist a Cognitive Radio system to
compare among the whole of available, candidate radio configurations and finally select the
best one to operate in.
The document discusses different meta-learning techniques for few-shot learning, including data augmentation, embedding, optimization, and semantic-based approaches. It provides examples of methods under each category and evaluates their performance on Omniglot and MiniImageNet datasets. While data augmentation and embedding techniques performed well on Omniglot, their accuracy was lower on MiniImageNet. Overall performance of state-of-the-art models remains far below human abilities, indicating room for improvement through hybrid models combining multiple technique
Fuzzy clustering has been widely studied and applied in a variety of key areas of science and
engineering. In this paper the Improved Teaching Learning Based Optimization (ITLBO)
algorithm is used for data clustering, in which the objects in the same cluster are similar. This
algorithm has been tested on several datasets and compared with some other popular algorithm
in clustering. Results have been shown that the proposed method improves the output of
clustering and can be efficiently used for fuzzy clustering.
This document discusses online feature selection (OFS) for data mining applications. It addresses two tasks of OFS: 1) learning with full input, where the learner can access all features to select a subset, and 2) learning with partial input, where only a limited number of features can be accessed for each instance. Novel algorithms are presented for each task, and their performance is analyzed theoretically. Experiments on real-world datasets demonstrate the efficacy of the proposed OFS techniques for applications in computer vision, bioinformatics, and other domains involving high-dimensional sequential data.
Neighborhood search methods with moth optimization algorithm as a wrapper met...IJECEIAES
Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flame Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Iaetsd an enhanced feature selection forIaetsd Iaetsd
The document discusses feature selection techniques for machine learning applications. It proposes an Enhanced Fast Clustering-based Feature Selection (EFAST) algorithm. The EFAST algorithm works in two steps: 1) features are clustered using graph-theoretic clustering methods, and 2) the most relevant representative feature strongly correlated with the target categories is selected from each cluster to form the optimal feature subset. Features from different clusters are relatively independent, so EFAST has a high chance of selecting a set of useful and independent features. The algorithm was tested on real-world data and showed improved performance over other feature selection methods by reducing features while also improving classifier performance.
Investigations on Hybrid Learning in ANFISIJERA Editor
Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN) is a network of interconnected artificial processing elements (called neurons) that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS), which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM) to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN) and RKLM can combine the advantages of two systems and avoid their disadvantages.
Soft Computing based Learning for Cognitive Radioidescitation
Over the last decade the world of wireless communications has been undergoing
some crucial changes, which have brought it at the forefront of international research and
development interest, eventually resulting in the advent of a multitude of innovative
technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh
networks and Software Defined Radio. Such a highly varying radio environment calls for
intelligent management, allocation and usage of a scarce resource, namely the radio
spectrum. One of the most prominent emerging technologies that promise to handle such
situations is Cognitive Radio. Cognitive Radio systems are based on Software Defined Radio
technology and utilize intelligent software packages that enrich their transceivers with the
highly attractive properties of self-awareness, adaptability and capability to learn. The
Cognitive Engine, the intelligent system behind the Cognitive Radio, combines sensing,
learning, and optimization algorithms to control and adapt the radio system from the
physical layer and up the communication stack. The integration of a learning engine can be
very important for improving the stability and reliability of the discovery and evaluation of
the configuration capabilities. To this effect, many different learning techniques are
available and can be used by a Cognitive Radio ranging from pure lookup tables to
arbitrary combinations of soft Computing techniques, which include among others:
Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy
systems, Hidden Markov Models, etc. The proposed work contributes in this direction,
aiming to develop a learning scheme and work towards solving problems related to learning
phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the
performance assessment work, conducted in order to design and use an appropriate
structure, while indicative results need to be presented and discussed in order to showcase
the benefits of incorporating such learning schemes into Cognitive Radio systems.
Subsequently feasibility of such learning schemes could be tested with simulations. In the
near future, such learning schemes are expected to assist a Cognitive Radio system to
compare among the whole of available, candidate radio configurations and finally select the
best one to operate in.
The document discusses different meta-learning techniques for few-shot learning, including data augmentation, embedding, optimization, and semantic-based approaches. It provides examples of methods under each category and evaluates their performance on Omniglot and MiniImageNet datasets. While data augmentation and embedding techniques performed well on Omniglot, their accuracy was lower on MiniImageNet. Overall performance of state-of-the-art models remains far below human abilities, indicating room for improvement through hybrid models combining multiple technique
Multi Label Spatial Semi Supervised Classification using Spatial Associative ...cscpconf
Multi-label spatial classification based on association rules with multi objective genetic
algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal
with multiple class labels problem. In this paper we adapt problem transformation for the multi
label classification. We use hybrid evolutionary algorithm for the optimization in the generation
of spatial association rules, which addresses single label. MOGA is used to combine the single
labels into multi labels with the conflicting objectives predictive accuracy and
comprehensibility. Semi supervised learning is done through the process of rule cover
clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is
simulated and the results are compared with MOGA based associative classifier, which out
performs the existing
03 fauzi indonesian 9456 11nov17 edit septianIAESIJEECS
Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
- The document proposes a new model called Performance Factors Analysis (PFA) as an alternative to the commonly used Knowledge Tracing (KT) and Learning Factors Analysis (LFA) models for adaptive instruction.
- PFA modifies LFA to make it sensitive to student performance (correct vs incorrect responses) in order to enable individualized modeling of student learning needed for adaptive tutoring, while retaining LFA's advantages for educational data mining.
- Comparison of PFA to LFA and a non-adaptive version of LFA on four datasets showed PFA performed comparably to LFA, demonstrating it can effectively capture individual student differences for adaptive instruction.
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.
This document discusses using machine learning clustering algorithms to analyze stock market data. It compares the K-means, COBWEB, DBSCAN, EM and OPTICS clustering algorithms in the WEKA tool on a stock market dataset containing 420 instances and 6 attributes. The K-means algorithm had the best performance with the lowest error and fastest runtime. It clustered the data into 4 groups in 0.16 seconds. The COBWEB algorithm clustered the data into 107 groups in 27.88 seconds. The DBSCAN algorithm found 21 clusters in 3.97 seconds. The paper concludes that K-means is best suited for stock market data mining applications due to its simplicity and speed compared to other algorithms.
Fuzzy modeling require two main steps which are structure identification and parameter optimization, the
first one determines the numbers of membership functions and fuzzy if-then rules, while the second
identifies a feasible set of parameters under the given structure. However, the increase of input dimension,
rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this
paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The
appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing
of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system.
First learning of the network structure by subtractive clustering, in order to define an optimal structure and
obtain small number of rules, then learning of parameters network by hybrid learning which combine the
gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents
parameters. The results obtained show the effectiveness of the method in terms of recognition rate and
number of fuzzy rules generated.
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.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC SYSTEM FOR TIME SERIES AND IDENTIF...ijfls
This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.
IRJET- Multi-Document Summarization using Fuzzy and Hierarchical ApproachIRJET Journal
This document discusses multi-document summarization using fuzzy and hierarchical approaches. It begins with an abstract describing multi-document summarization as extracting important information from multiple source documents to create a short summary. The introduction discusses the need for efficient multi-document summarization due to the large amount of online information. It then reviews related literature on multi-document summarization techniques including neuro-fuzzy approaches and modified K-nearest neighbor algorithms. Finally, it describes the proposed methodology which uses statistical approaches like similarity measures, page rank and expectation maximization to cluster sentences and extract a summary from the clustered sentences.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Oversampling technique in student performance classification from engineering...IJECEIAES
This document discusses various oversampling techniques for dealing with imbalanced data in student performance classification. It compares SMOTE, Borderline-SMOTE, SVMSMOTE, and ADASYN oversampling combined with MLP, gradient boosting, AdaBoost, and random forest classifiers. The results show that Borderline-SMOTE gave the best performance for predicting the minority (low performance) class according to several evaluation metrics. SVMSMOTE also performed well overall, particularly for recall, F1-measure, and AUC. Gradient boosting provided high and consistent precision, recall, F1-measure, and AUC across the different oversampling methods.
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.
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.
This document describes a meta-learning approach to scalable clustering using multiple independent base classifiers. The algorithm divides data into disjoint subsets to train separate classifiers, then cross-references their results to correlate categories. Experimental results on DNA splice junction and protein coding region datasets show classification accuracy improves over a single classifier, demonstrating the method can scale to large data.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
A Study on Machine Learning and Its WorkingIJMTST Journal
Machine learning (ML) is widely popular these days and used in wide variety of domains for prediction of
outcomes. In machine learning lot of algorithms exists for predicting the outcomes. But choosing the right
algorithm according to the domain plays a very important in deciding the performance of the algorithm. This
paper consists of five sections is organized in following way first section deals about collection of data from
various resources, second section deals about data cleaning, third part deals about choosing the correct ML
algorithm, fourth part deals about gaining knowledge from models and final part deals about data
visualization
Classification of Machine Learning AlgorithmsAM Publications
The goal of various machine learning algorithms is to device learning algorithms that learns automatically without any human intervention or assistance. The emphasis of machine learning is on automatic methods. Supervised Learning, unsupervised learning and reinforcement learning are discussed in this paper. Machine learning is the core area of Artificial Intelligence. Although a subarea of AI, machine learning also intersects broadly with other fields, especially statistics, but also mathematics, physics, theoretical computer science and more.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
Artificial Neural Networks used for learning algorithms in the field of Artificial Intelligence. BS(CS) level, semester 5th slides for the course Artificial Intelligence in Federal Urdu University of Arts Science and Technology.
Semi-supervised learning approach using modified self-training algorithm to c...IJECEIAES
Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Multi Label Spatial Semi Supervised Classification using Spatial Associative ...cscpconf
Multi-label spatial classification based on association rules with multi objective genetic
algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal
with multiple class labels problem. In this paper we adapt problem transformation for the multi
label classification. We use hybrid evolutionary algorithm for the optimization in the generation
of spatial association rules, which addresses single label. MOGA is used to combine the single
labels into multi labels with the conflicting objectives predictive accuracy and
comprehensibility. Semi supervised learning is done through the process of rule cover
clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is
simulated and the results are compared with MOGA based associative classifier, which out
performs the existing
03 fauzi indonesian 9456 11nov17 edit septianIAESIJEECS
Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
- The document proposes a new model called Performance Factors Analysis (PFA) as an alternative to the commonly used Knowledge Tracing (KT) and Learning Factors Analysis (LFA) models for adaptive instruction.
- PFA modifies LFA to make it sensitive to student performance (correct vs incorrect responses) in order to enable individualized modeling of student learning needed for adaptive tutoring, while retaining LFA's advantages for educational data mining.
- Comparison of PFA to LFA and a non-adaptive version of LFA on four datasets showed PFA performed comparably to LFA, demonstrating it can effectively capture individual student differences for adaptive instruction.
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.
This document discusses using machine learning clustering algorithms to analyze stock market data. It compares the K-means, COBWEB, DBSCAN, EM and OPTICS clustering algorithms in the WEKA tool on a stock market dataset containing 420 instances and 6 attributes. The K-means algorithm had the best performance with the lowest error and fastest runtime. It clustered the data into 4 groups in 0.16 seconds. The COBWEB algorithm clustered the data into 107 groups in 27.88 seconds. The DBSCAN algorithm found 21 clusters in 3.97 seconds. The paper concludes that K-means is best suited for stock market data mining applications due to its simplicity and speed compared to other algorithms.
Fuzzy modeling require two main steps which are structure identification and parameter optimization, the
first one determines the numbers of membership functions and fuzzy if-then rules, while the second
identifies a feasible set of parameters under the given structure. However, the increase of input dimension,
rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this
paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The
appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing
of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system.
First learning of the network structure by subtractive clustering, in order to define an optimal structure and
obtain small number of rules, then learning of parameters network by hybrid learning which combine the
gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents
parameters. The results obtained show the effectiveness of the method in terms of recognition rate and
number of fuzzy rules generated.
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.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
INTERVAL TYPE-2 INTUITIONISTIC FUZZY LOGIC SYSTEM FOR TIME SERIES AND IDENTIF...ijfls
This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.
IRJET- Multi-Document Summarization using Fuzzy and Hierarchical ApproachIRJET Journal
This document discusses multi-document summarization using fuzzy and hierarchical approaches. It begins with an abstract describing multi-document summarization as extracting important information from multiple source documents to create a short summary. The introduction discusses the need for efficient multi-document summarization due to the large amount of online information. It then reviews related literature on multi-document summarization techniques including neuro-fuzzy approaches and modified K-nearest neighbor algorithms. Finally, it describes the proposed methodology which uses statistical approaches like similarity measures, page rank and expectation maximization to cluster sentences and extract a summary from the clustered sentences.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Oversampling technique in student performance classification from engineering...IJECEIAES
This document discusses various oversampling techniques for dealing with imbalanced data in student performance classification. It compares SMOTE, Borderline-SMOTE, SVMSMOTE, and ADASYN oversampling combined with MLP, gradient boosting, AdaBoost, and random forest classifiers. The results show that Borderline-SMOTE gave the best performance for predicting the minority (low performance) class according to several evaluation metrics. SVMSMOTE also performed well overall, particularly for recall, F1-measure, and AUC. Gradient boosting provided high and consistent precision, recall, F1-measure, and AUC across the different oversampling methods.
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.
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.
This document describes a meta-learning approach to scalable clustering using multiple independent base classifiers. The algorithm divides data into disjoint subsets to train separate classifiers, then cross-references their results to correlate categories. Experimental results on DNA splice junction and protein coding region datasets show classification accuracy improves over a single classifier, demonstrating the method can scale to large data.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
A Study on Machine Learning and Its WorkingIJMTST Journal
Machine learning (ML) is widely popular these days and used in wide variety of domains for prediction of
outcomes. In machine learning lot of algorithms exists for predicting the outcomes. But choosing the right
algorithm according to the domain plays a very important in deciding the performance of the algorithm. This
paper consists of five sections is organized in following way first section deals about collection of data from
various resources, second section deals about data cleaning, third part deals about choosing the correct ML
algorithm, fourth part deals about gaining knowledge from models and final part deals about data
visualization
Classification of Machine Learning AlgorithmsAM Publications
The goal of various machine learning algorithms is to device learning algorithms that learns automatically without any human intervention or assistance. The emphasis of machine learning is on automatic methods. Supervised Learning, unsupervised learning and reinforcement learning are discussed in this paper. Machine learning is the core area of Artificial Intelligence. Although a subarea of AI, machine learning also intersects broadly with other fields, especially statistics, but also mathematics, physics, theoretical computer science and more.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
Artificial Neural Networks used for learning algorithms in the field of Artificial Intelligence. BS(CS) level, semester 5th slides for the course Artificial Intelligence in Federal Urdu University of Arts Science and Technology.
Semi-supervised learning approach using modified self-training algorithm to c...IJECEIAES
Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
1. The document presents a hybrid algorithm that combines Kernelized Fuzzy C-Means (KFCM), Hybrid Ant Colony Optimization (HACO), and Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to improve clustering of electrocardiogram (ECG) beat data.
2. The algorithm maps data into a higher dimensional space using kernel functions to make clusters more linearly separable, addresses issues with KFCM being sensitive to initialization and prone to local minima.
3. It uses HACO to optimize cluster centers and membership degrees, and FAPSO to evaluate fitness values and optimize weight vectors, forming usable clusters for applications like ECG classification.
Association rule discovery for student performance prediction using metaheuri...csandit
According to the increase of using data mining tech
niques in improving educational systems
operations, Educational Data Mining has been introd
uced as a new and fast growing research
area. Educational Data Mining aims to analyze data
in educational environments in order to
solve educational research problems. In this paper
a new associative classification technique
has been proposed to predict students final perform
ance. Despite of several machine learning
approaches such as ANNs, SVMs, etc. associative cla
ssifiers maintain interpretability along
with high accuracy. In this research work, we have
employed Honeybee Colony Optimization
and Particle Swarm Optimization to extract associat
ion rule for student performance prediction
as a multi-objective classification problem. Result
s indicate that the proposed swarm based
algorithm outperforms well-known classification tec
hniques on student performance prediction
classification problem.
ASSOCIATION RULE DISCOVERY FOR STUDENT PERFORMANCE PREDICTION USING METAHEURI...cscpconf
According to the increase of using data mining techniques in improving educational systems
operations, Educational Data Mining has been introduced as a new and fast growing research
area. Educational Data Mining aims to analyze data in educational environments in order to
solve educational research problems. In this paper a new associative classification technique
has been proposed to predict students final performance. Despite of several machine learning
approaches such as ANNs, SVMs, etc. associative classifiers maintain interpretability along
with high accuracy. In this research work, we have employed Honeybee Colony Optimization
and Particle Swarm Optimization to extract association rule for student performance prediction
as a multi-objective classification problem. Results indicate that the proposed swarm based
algorithm outperforms well-known classification techniques on student performance prediction
classification problem.
IRJET-Performance Enhancement in Machine Learning System using Hybrid Bee Col...IRJET Journal
The document proposes a Hybrid Bee Colony based Neural Network (HBCNN) technique for performance enhancement in machine learning systems. The HBCNN technique has three phases: 1) data preprocessing, 2) training an artificial neural network using a bee colony algorithm, and 3) dynamic testing of the trained neural network. The proposed technique is tested on a cancer dataset and its performance is compared to a K-nearest neighbors technique. The HBCNN technique aims to address issues with existing approaches like reduced prediction accuracy and high computational costs.
Evolutionary Algorithm for Optimal Connection Weights in Artificial Neural Ne...CSCJournals
A neural network may be considered as an adaptive system that progressively self-organizes in order to approximate the solution, making the problem solver free from the need to accurately and unambiguously specify the steps towards the solution. Moreover, Evolutionary Artificial Neural Networks (EANNs) have the ability to progressively improve their performance on a given task by executing learning. An evolutionary computation gives adaptability for connection weights using feed forward architecture. In this paper, the use of evolutionary computation for feed-forward neural network learning is discussed. To check the validation of proposed method, XOR benchmark problem has been used. The accuracy of the proposed model is more satisfactory as compared to gradient method.
This document provides an overview of machine learning and case-based reasoning. It begins with an introduction to machine learning that defines the term and discusses common approaches like decision trees, artificial neural networks, genetic algorithms, and reinforcement learning. It then provides a more in-depth discussion of case-based reasoning, covering how it draws from psychological models of human reasoning and problem-solving. The document discusses both the advantages and disadvantages of machine learning approaches.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
This document proposes a general active learning framework called expected loss optimization (ELO) for ranking problems. The ELO framework uses an ensemble of ranking models to select examples that are expected to minimize a chosen loss function, such as discounted cumulative gain (DCG) loss. The document presents an algorithm called expected DCG loss optimization (ELO-DCG) that selects examples based on expected DCG loss. It also describes a two-stage algorithm that first selects queries and then documents to address sample dependence. Finally, it discusses how the algorithms can be modified to handle skewed grade distributions in ranking data.
This document summarizes a study on using a fuzzy total margin based support vector machine (FTM-SVM) approach to handle class imbalance in machine learning classification problems. It discusses how traditional SVM classifiers can overfit to the majority class in imbalanced data sets. The proposed FTM-SVM method aims to address this issue by incorporating a total margin algorithm, different cost functions, and fuzzy membership functions to reduce the effect of outliers and noise on the minority class. The paper evaluates the FTM-SVM approach on artificial and imbalanced data sets, finding it achieves higher performance measures than some existing class imbalance learning methods.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
Parallel Genetic Algorithms for University Scheduling ProblemIJECEIAES
University scheduling timetabling problem, falls into NP hard problems. Re-searchers have tried with many techniques to find the most suitable and fastest way for solving the problem. With the emergence of multi-core systems, the parallel implementation was considered for finding the solution. Our approaches attempt to combine several techniques in two algorithms: coarse grained algorithm and multi thread tournament algorithm. The results obtained from two algorithms are compared, using an algorithm evaluation function. Considering execution time, the coarse grained algorithm performed twice better than the multi thread algorithm.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
An Automatic Medical Image Segmentation using Teaching Learning Based Optimiz...idescitation
Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
Text document clustering and similarity detection is the major part of document management, where every document should be identified by its key terms and domain knowledge. Based on the similarity, the documents are grouped into clusters. For document similarity calculation there are several approaches were proposed in the existing system. But the existing system is either term based or pattern based. And those systems suffered from several problems. To make a revolution in this challenging environment, the proposed system presents an innovative model for document similarity by applying back propagation time stamp algorithm. It discovers patterns in text documents as higher level features and creates a network for fast grouping. It also detects the most appropriate patterns based on its weight and BPTT performs the document similarity measures. Using this approach, the document can be categorized easily. In order to perform the above, a new approach is used. This helps to reduce the training process problems. The above framework is named as BPTT. The BPTT has implemented and evaluated using dot net platform with different set of datasets.
A new approachto image classification based on adeep multiclass AdaBoosting e...IJECEIAES
In recent years, deep learning methods have been developed in order to solve the problems. These methods were effective in solving complex problems. Convolution is one of the learning methods. This method is applied in classifying and processing of images as well. Hybrid methods are another multi-component machine learning method. These methods are categorized into independent and dependent types. Ada-Boosting algorithm is one of these methods. Today, the classification of images has many applications. So far, several algorithms have been presented for binary and multi-class classification. Most of the above-mentioned methods have a high dependence on the data. The present study intends to use a combination of deep learning methods and associated hybrid methods to classify the images. It is presumed that this method is able to reduce the error rate in images classification. The proposed algorithm consists of the Ada-Boosting hybrid method and bi-layer convolutional learning method. The proposed method was analyzed after it was implemented on a multi-class Mnist data set and displayed the result of the error rate reduction. The results of this study indicate that the error rate of the proposed method is less than Ada-Boosting and convolution methods. Also, the network has more stability compared to the other methods.
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
Similar to A hybrid constructive algorithm incorporating teaching-learning based optimization for neural network training (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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%.
Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
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problem-dependent parameters are to be properly identified in order to obtain an acceptable network with
a satisfactory performance. This makes it difficult to be used in real-world applications [4].
This paper portrays a combination of random search procedures and systematic methods, proposing
hybridizing improved teaching-learning algorithms with constructive algorithms for the purpose of ANN
design. The hybrid is advantageous, for teaching-learning algorithm is a parameter-independent optimization
algorithm that balance between exploration and exploitation. Meanwhile, constructive algorithms are adopted
to select an appropriate ANN architecture. Since using constructive algorithms is cost-effective in terms of
the training-time and complexity of ANN, it hinders the production of networks with an inefficient very large
architecture. This paper, with the aim of simultaneously optimizing the ANN weights and architecture,
combines training and constructive algorithms applied to ten classification problems and two-time series
prediction problems, as the benchmark. After evaluating the performance of proposed hybrid algorithms and
comparing their results, it was found that the proposed method outperformed other algorithms. The proposed
combination method proves to have a lower mean error in most cases. The rest of this study is organized as
follows: Section 2 provides a brief description of the algorithms that we provided. Then, in Section 3,
a hybrid proposed method to ANN optimization is presented. In Section 4, the experimental results of the
application of the proposed approaches to the ANN problems are reported, and finally, the conclusion is
drawn in the last section.
2. ALGORITHM DESCRIPTION
2.1. Improved teaching-learning based optimization (ITLBO)
Although TLBO provides high-quality solutions in the least amount of time and has a great stability
in convergence [5], in the learner phase of this algorithm, learners randomly choose another learner from
the population. This difficulty leads to a lack of balance between the two concepts of diversity and
convergence. ITLBO with an improvement into basic TLBO overcomes this difficulty. In this algorithm,
the teacher phase is the same as the teacher phase in the basic TLBO algorithm and the learner phase is
expressed as follows. The ITLBO has been developed to improve the weaknesses of TLBO algorithm;
for example, in TLBO random choices due to low local search capability, but in ITLBO with addition
concept of neighborhood we trying to reduce random choices and utilize of neighborhood abilities. This issue
increases local search and global search capability. The main sections of ITLBO are as follows:
2.1.1. ITLBO learner phase
In this phase, each learner is encoded with an integer and placed in a rectangular array. learners may
learn from their neighbors or from the best individual in whole class. This process is based on local search
ability; furthermore, balance between global search and local search ability is applied. In local search, each
learner updates his position with Pc probability by the best learner in his neighborhood (or 𝑋𝑖,𝑡𝑒𝑎𝑐ℎ𝑒𝑟) and also
global best learner that in population.
𝑋𝑖,𝑛𝑒𝑤 = 𝑋𝑖,𝑜𝑙𝑑 + 𝑟2. (𝑋𝑖,𝑡𝑒𝑎𝑐ℎ𝑒𝑟 − 𝑋𝑖,𝑜𝑙𝑑) + 𝑟3. (𝑋𝑡𝑒𝑎𝑐ℎ𝑒𝑟 − 𝑋𝑖,𝑜𝑙𝑑) (1)
Where 𝑋𝑖,𝑡𝑒𝑎𝑐ℎ𝑒𝑟 is the teacher in 𝑋𝑖 neighborhood, 𝑋𝑡𝑒𝑎𝑐ℎ𝑒𝑟 is teacher of whole class, 𝑟2 , 𝑟3 are random
numbers in the range of (0, 1). The new position of each learner will be accepted if its fitness value has
improved. In the concept of global search, if Pc probability don’t meet, each learner chooses a random
learner (𝑋𝑗) from the whole class to provide the learning goal, if 𝑋𝑗 is better than 𝑋𝑖, or otherwise, learning
occurs according to learner phase in basic TLBO. Therefore, using these operations both local and global
search capability will be obtained. All the accepted learners at the end of learner phase are preserved. Due to
the enhanced exploitation ability along with the exploration ability, which already existed in the learning
phase of the original algorithm, we use the concept of neighborhood in the classroom. For each individual in
the population exist a number of neighborhood member that learn from the best one. For maintain of
diversity after a number of iterations the neighborhood members of each individual are changed. This issue
balance between the exploration and exploiting abilities. Other advantage there is in this algorithm, when
a new position is obtained for each member, it may lead to the production of decision variables values that
are out of the range of the definition interval. In this case, most researchers use the convergence approach to
the upper and lower bound according to algorithm, but this method is Old and disabled method witch cause
algorithm to local optima. In the improved teaching-learning based optimization method, we use modified
technique to check boundaries of the variables [6]. Its advantage is avoiding equalization of the decision
variables.
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3727
2.2. Modified MOST algorithm (MMOST)
Determining the architecture of artificial networks has lured many researchers in the field in recent
years. We used Multiple Operators using Statistical Tests algorithm MOST [7]. In MOST algorithm, there
isn’t any controlled method for change struture. This algorithm may have large changes in network structure
during the algorithm. Another weakness of this algorithm is the addition of layers frequently without any
condition to control. In modified MOST algorithm, the operator pool was removed. For changing structure
neurons are added one after another. Selecting the new structures is done more carefully by adding multiple
conditions. At the beginning, algorithm starts with a single hidden layer network by the minimum number of
neurons. We chose one of popular approach for allowed minimum number that is the average of number of
output layer and input layer. Network in the first step has a single hidden layer and neurons are added
continually to the hidden layer to obtain a proper structure of the network. To avoid creating very large
structures for networks, the neurons are added to single hidden layer of the network until they don’t exceed
Max-hidden number. In fact, networks with very large structure not only don’t have good generalizability,
but they also increase the computational time of the algorithm. To eliminate this weakness, we add
the second layer to network structure to create proper architecture with a probability less than P. after adding
the second layer, the number of neurons in each hidden layer is set by min-hidden. MMOST constructive
algorithm chooses the best architecture between constructed structures. So, as noted above, the differences
between the MMOST and MOST algorithm are as follows: operator’s pool is deleted; neurons are
continually added; and there is a more precise choice between the three previous, current and the candidate
architectures.
3. THE PROPOSED METHOD
In this paper, we proposed a combination algorithm for producing a neural network with proper
structure and weights, to simultaneous optimization of weights and structure. For this purpose, a combination
of the modified MOST constructive algorithm with an improved version of the training algorithm was
proposed. The role of the constructive algorithm in the proposed algorithm is to construct different structures
in order to select the proper one, which is carried out by using a switching systematic approach between the
various structures allowed for the neural network. On the other hand, the role of training algorithm is to find
optimal weights for the structure that is created by the constructive algorithm. Using constructive algorithms
in creating a network architecture reduces computational cost and complexity. But using these algorithms in
solving noisy problems [8] has failed, which in combination with other techniques, such as the use of
evolutionary algorithms, can be effective in improving the constructive algorithm. In addition, we have made
some modifications on the MOST constructive algorithm. For a more detailed description, the pseudo code of
the proposed hybrid algorithms is shown in Figure 1. In other words, in order to clarify the combination of
evolutionary training algorithms and constructive algorithms, we showed the process in flowchart by
Figure 2.
Figure 1. Pseudo code combined algorithm
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Figure 2. Flowchart of hybrid algorithm
4. COMPARISON RESULTS
In this section, we evaluate the effectiveness of proposed hybrid methods. These algorithms are
applied to ten classification problem and two time series prediction problems. We compare the performance
of the proposed hybrid algorithms first with each other and then with other available methods.
4.1. Definition of classification and time series prediction problems
The task of assigning a sample to a proper group, based on the characteristics of describing that
object in a problem, is defined as classification. The classification problems used in this article include iris,
diabetes diagnosis, thyroid, breast cancer, credit card, glass, heart, wine, page blocks, and liver. These
classification problems are taken from the UCI machine learning repository [9]. But the time series prediction
problems use a specific model to predict future values based on their previous values. The first is the Gas
Furnace Dataset [10], which is compiled from Jenkins's Book of Time Series Analysis. It contains gas
content and CO2 percentage in gas, and another is a Mackey glass dataset obtained from the below
differential equation:
𝑑𝑥(𝑡)
𝑑𝑡
= −𝑏𝑥(𝑡) +
𝑎𝑥(𝑡−𝑡 𝑑)
1+𝑥10(𝑡−𝑡 𝑑)
(2)
All proposed hybrid algorithms in this article have been implemented using MATLAB software and
have used 30 time run to evaluate the performance of these methods. The 4-fold-cross-validation method has
been used to divide the original dataset into two training and testing sets. This method can effectively prevent
trapping to local minima. Because both the training and testing samples contribute to learning as much as
possible, it can provide a satisfactory learning effect. The average error is obtained from the 4-fold-cross-
validation which is presented as the final error of the network. In addition, the input dataset to the neural
network is normalized using the min-max normalization method to the interval [-1.1]. The results of
the comparison are presented in two parts. First, the proposed algorithms are compared with each other, and
then the best proposed method is compared with the existing methods.
4.2. Comparing proposed methods with each other
Each of these algorithms has been executed 30 times, and the results of the experiments have been
compared with each other according to three criteria: classification error percentage of training and testing
data and complexity percentage. The function of error calculation For the Mackey glass is RMSE and for gas
furnace is MSE. First, we compare the performance of two kind of training algorithm that consist of classic
training algorithm (back- propagation) and evolutionary training algorithm (improved teaching learning-
based optimization). The results from Table 1 show that the ITLBO algorithm has a higher efficiency for
most data sets. According to Table 1, the ITLBO algorithm for all of classification problems has better
performance than the Bp algorithm, then in part2 from Table 1 we showed the results of comparing proposed
hybrid algorithms with each other. All the results are based on three characteristics (parameters) of training
and testing error for classification, MSE error and complexity. To better demonstrate the superior algorithm,
we did rank average test, and the rank average for different data set was presented in Table 2. As can be seen
5. Int J Elec & Comp Eng ISSN: 2088-8708
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from Table 2, MCO-ITLBO has gained the first rank for all of characteristic. To evaluate whether the MCO-
ITLBO results are significantly better than other approaches, we calculated the p-value test with a significant
level of 0.05 for data sets. The calculated P- values for MCO-ITLBO are shown in Table 3 in comparison
with other algorithms. The best results are bolded in the tables. In Figures 3 the box plot graphs showed
the results of the distribution of training and testing errors for the whole data set for 30 times running.
The charts show that MCO-ITLBO is superior in most cases.
Table 1. Average results of 30 runs of two kind training algorithm (part1) and each hybrid algorithm (part2)
Dataset Criteria BP ITLBO
(part1)
MCO-
TLBO
MCO-
ITLBO
MCO-BP
(part 2)
1. Iris
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
5.3125
7.5468
0.0024
0.4892
fix
0.9516
2.1440
3.5642e-09
1.7729e-06
fix
0.7819
3.2752
6.7324e-05
0.0721
26.4667
0.0138
2.2975
6.2480e-07
3.3917e-03
21.5806
7.6061
9.8990
1.0290e-02
1.2864
23.4667
2. Diabetes
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
31.2322
35.2887
0.6013
0.6933
fix
18.6897
27.8473
1.1637e-06
7.1247e-05
fix
19.5282
29.3795
1.529e-08
0.00179
109.1333
18.8775
23.3906
2.3468e-10
4.1822e-8
58.4194
28.0176
36.8359
6.3375e-04
1.3349
129.2333
3. Thyroid
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
17.8340
16.5672
0.1399
1.5968
fix
6.7798
12.5903
1.3987e-05
2.5786e-04
fix
8.2215
11.1318
4.8976e-06
0.00402
521.1
5.6059
6.5800
5.6589e-09
2.6158e-07
133.6452
18.3961
22.3837
3.8605e-03
1.4095
168.2235
4. Cancer
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
20.9419
16.3510
0.1437
0.8224
fix
1.9672
4.5018
3.0600e-04
0.0220
fix
1.7213
8.594
2.3604e-07
0.00336
257.9333
1.0843
2.0729
2.5695e-11
7.3679e-6
67.4194
21.5350
29.5131
1.2626e-03
7.4285
77.4545
5. Card
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
22.5247
23.8126
0.0258
1.1437
fix
15.7492
17.3196
3.3272e-09
3.0939e-05
fix
12.6111
24.3478
2.2402e-04
0.0184
452.6667
12.2379
13.5889
7.2066e-08
1.9381e-03
127.4194
19.9432
30.4480
3.4985e-05
3.0699
183.5455
6. Glass
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
36.7770
54.8387
0.4235
0.8631
fix
18.3169
36.9146
1.0244e-06
0.1431
fix
18.2252
31.7172
9.2081e-04
0.0964
392.6667
17.0881
22.0733
2.3845e-10
3.269e-06
123.3548
66.8305
67.9063
1.4604e-04
3.7141
104.7273
7. Heart
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
21.2208
23.6746
0.0022
0.8086
fix
10.4473
20.9877
7.4441e-11
0.0156
fix
12.1587
20
9.4259e-05
8.7661e-03
198.7333
11.2647
13.3325
8.2271e-12
1.0260e-07
93.3548
20.2982
23.4254
2.4283e-06
0.2213
100.9091
8. Wine
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
17.0641
23.1063
0.0215
1.0307
fix
4.9978
13.1856
1.6924e-09
0.0089
fix
0.51491
3.8182
3.9983e-08
1.2784e-03
286.9333
0.4687
3.8094
1.9516e-11
5.9690e-06
117.6129
21.0421
23.5537
1.5403e-06
5.1870
98.9091
9. Page-blocks
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
10.0000
12.2151
0.0993
0.8666
fix
6.1260
7.2960
3.3001e-07
5.1312e-04
fix
6.918
13.309
2.6707e-09
0.02848
328.9333
6.3471
6.4575
2.1143e-19
1.0861e-08
107.7097
12.8043
16.6030
1.3954e-10
0.0584
64.0909
10. Liver
Training error (%Class)
Testing error (%Class)
Training error (MSE)
Testing error (MSE)
Connection
37.6344
38.5543
0.0919
0.5596
fix
24.3154
25.8622
7.0024e-09
0.0016
fix
23.9419
38.75
2.3477e-05
0.0267
86.4667
22.9577
27.9210
5.4317e-07
2.419e-03
47.2903
45.2282
43.8811
9.2163e-03
0.3244
31.2727
11. Mackey-
glass
Training error (%Class)
Testing error (%Class)
Connection
8.3054e-06
0.0070
fix
3.7775e-04
0.0280
fix
2.2489e-04
2.3505e-04
47.75
1.5475e-05
2.0770e-04
40.8710
3.8468e-06
0.0560
16.4839
12. Gas furnace
Training error (%Class)
Testing error (%Class)
Connection
3.5329e-04
4.6352
fix
0.0071
0.1987
fix
1.5932e-3
1.6671e-3
62.6
1.2101e-04
3.2001e-03
47.1935
6.846e-04
0.0228
15.4516
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Table 2. Average of the ranks for proposed algorithms
Training error (Classification) Algorithm MCO-TLBO MCO-ITLBO MCO-Bp
Rank 2.0000 1.0000 3.0000
Testing error (Classification) Algorithm MCO-TLBO MCO-ITLBO MCO-Bp
Rank 2.0000 1.0000 3.0000
Training error (Mse) Algorithm MCO-TLBO MCO-ITLBO MCO-Bp
Rank 2.4000 1.0000 2.6000
Testing error (Mse) Algorithm MCO-TLBO MCO-ITLBO MCO-Bp
Rank 2.0000 1.0000 3.0000
Number of connections Algorithm MCO-TLBO MCO-ITLBO MCO-Bp
Rank 2.8000 1.4000 1.8000
Table 3. P-value results for pairwise comparison of MCO-ITLBO versus other algorithms by wilcoxon test
Dataset Criteria MCO-TLBO MCO-Bp
1. Iris testing error 0.65641 9.0733e-12
connection 0.1258 1.2196e-11
2. Diabetes testing error 0.093779 2.9174e-11
connection 0.30617 9.8375e-09
3. Thyroid testing error 1.2369e-05 5.9941e-07
connection 0.00010012 7.0643e-09
4. Cancer testing error 0.40898 2.6757e-11
connection 0.31604 5.0422e-11
5. Card testing error 0.77239 5.5893e-11
connection 0.1433 2.9321e-11
6. Glass testing error 0.03935 2.8502e-11
connection 0.006635 3.9787e-10
7. Heart testing error 0.3416 2.7722e-11
connection 0.51018 1.497e-10
8. Wine testing error 0.043924 2.3481e-11
connection 0.02634 3.1372e-11
9. Page-blockes testing error 0.00338 3.0123e-11
connection 0.04819 2.9991e-11
10. liver testing error 0.74965 2.8991e-11
connection 0.69925 3.6305e-09
11. Mackey-Glass testing error 0.0011143 0.007959
connection 0.19073 0.00058737
12. Gas Furnace testing error 0.14532 5.462e-06
connection 3.352e-08 0.0050842
Figure 3. Box plots of training and testing errors for all datasets
7. Int J Elec & Comp Eng ISSN: 2088-8708
A hybrid constructive algorithm incorporating teaching-learning based … (Mahdie Khorashadizade)
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Figure 3. Box plots of training and testing errors for all datasets (continue)
4.3. Results of comparing the best proposed hybrid method with other methods
In this section, we compare our hybrid algorithms with other literature methods in Table 4.
The percentage of training error and testing error collection in this table. Each article works on a batch of
datasets. The cells of this table that don’t have any value (that indicate with an - icon) shows that these values
are missing data or belong to a dataset that articles don’t work on this. We give a brief description of
the comparative approaches as follows. We reference all the approaches that we compared our best proposed
method with them.
8. ISSN: 2088-8708
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Table 4. Comparing the results of best algorithm with other methods in literature
5. CONCLUSION
In this paper, we proposed a hybridization of training algorithms and constructive algorithms to
simultaneously determine the weight and structure of the neural network. The goal is to examine
hybridization of a deterministic and systematic procedure (constructive algorithm) with random search
(evolutionary algorithm) for neural network optimization. Combined methods include the base and improved
version of the TLBO algorithm with the MMOST algorithms. Then we compared hybrid algorithms,
and selected the superior algorithm in classification and time series prediction problems. The results of
the comparison illustrate the superior performance belongs to the MCO-ITLBO algorithm. This version has
a powerful training algorithm against early convergence, and balances between exploitation and exploration.
This algorithm in combination with the MMOST constructive algorithm, more effectively selects the optimal
network structure. We have also verified these results with statistical tests, and finally this algorithm was
compared with other methods in literature and it has been proven that it is more convenient than other
algorithms for classification and time series prediction error. These promising results motivate us to find
ways to change our path to future work. This development can be using chaotic (disorder) mappings in
this method.
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