Pub Date : 2021-05-30DOI: 10.1142/s1469026822500092
Xiongshi Deng, Min Li, Lei Wang, Qikang Wan
Feature selection is a preprocessing step that plays a crucial role in the domain of machine learning and data mining. Feature selection methods have been shown to be effective in removing redundant and irrelevant features, improving the learning algorithm’s prediction performance. Among the various methods of feature selection based on redundancy, the fast correlation-based filter (FCBF) is one of the most effective. In this paper, we developed a novel extension of FCBF, called resampling FCBF (RFCBF) that combines resampling technique to improve classification accuracy. We performed comprehensive experiments to compare the RFCBF with other state-of-the-art feature selection methods using three competitive classifiers (K-nearest neighbor, support vector machine, and logistic regression) on 12 publicly available datasets. The experimental results show that the RFCBF algorithm yields significantly better results than previous state-of-the-art methods in terms of classification accuracy and runtime.
{"title":"RFCBF: enhance the performance and stability of Fast Correlation-Based Filter","authors":"Xiongshi Deng, Min Li, Lei Wang, Qikang Wan","doi":"10.1142/s1469026822500092","DOIUrl":"https://doi.org/10.1142/s1469026822500092","url":null,"abstract":"Feature selection is a preprocessing step that plays a crucial role in the domain of machine learning and data mining. Feature selection methods have been shown to be effective in removing redundant and irrelevant features, improving the learning algorithm’s prediction performance. Among the various methods of feature selection based on redundancy, the fast correlation-based filter (FCBF) is one of the most effective. In this paper, we developed a novel extension of FCBF, called resampling FCBF (RFCBF) that combines resampling technique to improve classification accuracy. We performed comprehensive experiments to compare the RFCBF with other state-of-the-art feature selection methods using three competitive classifiers (K-nearest neighbor, support vector machine, and logistic regression) on 12 publicly available datasets. The experimental results show that the RFCBF algorithm yields significantly better results than previous state-of-the-art methods in terms of classification accuracy and runtime.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122449493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-19DOI: 10.1142/S1469026821500097
A. Yosef, E. Shnaider, Rimona Palas, Amos Baranes
This study presents a decision-support method to estimate the next year performance of corporate Operating Income Margin (OIM). It is based on a unique combination of cross-section model and the ru...
{"title":"Decision Support System Based on Fuzzy Logic for Assessment of Expected Corporate Income Performance","authors":"A. Yosef, E. Shnaider, Rimona Palas, Amos Baranes","doi":"10.1142/S1469026821500097","DOIUrl":"https://doi.org/10.1142/S1469026821500097","url":null,"abstract":"This study presents a decision-support method to estimate the next year performance of corporate Operating Income Margin (OIM). It is based on a unique combination of cross-section model and the ru...","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"34 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124982408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-06DOI: 10.1142/S1469026821500103
C. M. M. Refat, N. Azlan
Sensor-based Facial expression recognition (FER) is an attractive research topic. Nowadays, FER is used for different application such as smart environments and healthcare solutions. The machine ca...
{"title":"Stretch Sensor-Based Facial Expression Recognition and Classification Using Machine Learning","authors":"C. M. M. Refat, N. Azlan","doi":"10.1142/S1469026821500103","DOIUrl":"https://doi.org/10.1142/S1469026821500103","url":null,"abstract":"Sensor-based Facial expression recognition (FER) is an attractive research topic. Nowadays, FER is used for different application such as smart environments and healthcare solutions. The machine ca...","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131279588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-22DOI: 10.1142/S1469026821500085
R. Mehta
In recent times, the application of autonomic soft computing techniques for design and optimization of wireless access networks is progressively becoming prevalent. These computational learning tec...
近年来，自主软计算技术在无线接入网络设计和优化中的应用日益普及。这些计算学习技术…
{"title":"Hybrid Fuzzy-Genetic Model for Fitness-Based Performance Optimization in Wireless Networks","authors":"R. Mehta","doi":"10.1142/S1469026821500085","DOIUrl":"https://doi.org/10.1142/S1469026821500085","url":null,"abstract":"In recent times, the application of autonomic soft computing techniques for design and optimization of wireless access networks is progressively becoming prevalent. These computational learning tec...","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126248926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1469026820500315
E. Mohamed, M. Moussa, M. Haggag
Sentiment analysis (SA) is a technique that lets people in different fields such as business, economy, research, government, and politics to know about people’s opinions, which greatly affects the process of decision-making. SA techniques are classified into: lexicon-based techniques, machine learning techniques, and a hybrid between both approaches. Each approach has its limitations and drawbacks, the machine learning approach depends on manual feature extraction, lexicon-based approach relies on sentiment lexicons that are usually unscalable, unreliable, and manually annotated by human experts. Nowadays, word-embedding techniques have been commonly used in SA classification. Currently, Word2Vec and GloVe are some of the most accurate and usable word embedding techniques, which can transform words into meaningful semantic vectors. However, these techniques ignore sentiment information of texts and require a huge corpus of texts for training and generating accurate vectors, which are used as inputs of deep learning models. In this paper, we propose an enhanced ensemble classifier framework. Our framework is based on our previously published lexicon-based method, bag-of-words, and pre-trained word embedding, first the sentence is preprocessed by removing stop-words, POS tagging, stemming and lemmatization, shortening exaggerated word. Second, the processed sentence is passed to three modules, our previous lexicon-based method (Sum Votes), bag-of-words module and semantic module (Word2Vec and Glove) and produced feature vectors. Finally, the previous features vectors are fed into 11 different classifiers. The proposed framework is tested and evaluated over four datasets with five different lexicons, the experiment results show that our proposed model outperforms the previous lexicon based and the machine learning methods individually.
{"title":"An Enhanced Sentiment Analysis Framework Based on Pre-Trained Word Embedding","authors":"E. Mohamed, M. Moussa, M. Haggag","doi":"10.1142/s1469026820500315","DOIUrl":"https://doi.org/10.1142/s1469026820500315","url":null,"abstract":"Sentiment analysis (SA) is a technique that lets people in different fields such as business, economy, research, government, and politics to know about people’s opinions, which greatly affects the process of decision-making. SA techniques are classified into: lexicon-based techniques, machine learning techniques, and a hybrid between both approaches. Each approach has its limitations and drawbacks, the machine learning approach depends on manual feature extraction, lexicon-based approach relies on sentiment lexicons that are usually unscalable, unreliable, and manually annotated by human experts. Nowadays, word-embedding techniques have been commonly used in SA classification. Currently, Word2Vec and GloVe are some of the most accurate and usable word embedding techniques, which can transform words into meaningful semantic vectors. However, these techniques ignore sentiment information of texts and require a huge corpus of texts for training and generating accurate vectors, which are used as inputs of deep learning models. In this paper, we propose an enhanced ensemble classifier framework. Our framework is based on our previously published lexicon-based method, bag-of-words, and pre-trained word embedding, first the sentence is preprocessed by removing stop-words, POS tagging, stemming and lemmatization, shortening exaggerated word. Second, the processed sentence is passed to three modules, our previous lexicon-based method (Sum Votes), bag-of-words module and semantic module (Word2Vec and Glove) and produced feature vectors. Finally, the previous features vectors are fed into 11 different classifiers. The proposed framework is tested and evaluated over four datasets with five different lexicons, the experiment results show that our proposed model outperforms the previous lexicon based and the machine learning methods individually.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128377770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1469026820500273
Liang Ma, Hailin Jiang, Wanli Yang, Quanjie Zhu
Accurate identification of Golgi protein types can provide useful clues to reveal the correlation between GA dysfunction and disease pathology and improve the ability to develop more effective treatments for the diseases. This paper introduces an effective and robust method to classify Golgi protein type with traditional machine learning algorithms. In which various features such as n-GDip, DCCA, psePSSM were used as training features and SVM with linear kernel was employed as a classifier. To solve the imbalance problem of the benchmark datasets, the oversampling technique SMOTE was adopted. To deal with the huge amount of features, the PCA algorithm and Fisher feature selection method were adopted to reduce feature dimensions and remove redundant features. The experimental results show that the proposed method had a further improvement compared with other traditional machine learning methods in 10-fold cross-validation, Jackknife cross-validation and independent testing, which means a further step for the clinical application of computational methods to predict the Golgi protein types.
{"title":"A Novel Method to Identify Golgi Protein Types Based on Hybrid Feature and SVM Algorithm","authors":"Liang Ma, Hailin Jiang, Wanli Yang, Quanjie Zhu","doi":"10.1142/s1469026820500273","DOIUrl":"https://doi.org/10.1142/s1469026820500273","url":null,"abstract":"Accurate identification of Golgi protein types can provide useful clues to reveal the correlation between GA dysfunction and disease pathology and improve the ability to develop more effective treatments for the diseases. This paper introduces an effective and robust method to classify Golgi protein type with traditional machine learning algorithms. In which various features such as n-GDip, DCCA, psePSSM were used as training features and SVM with linear kernel was employed as a classifier. To solve the imbalance problem of the benchmark datasets, the oversampling technique SMOTE was adopted. To deal with the huge amount of features, the PCA algorithm and Fisher feature selection method were adopted to reduce feature dimensions and remove redundant features. The experimental results show that the proposed method had a further improvement compared with other traditional machine learning methods in 10-fold cross-validation, Jackknife cross-validation and independent testing, which means a further step for the clinical application of computational methods to predict the Golgi protein types.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124750997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1469026820500303
Min Ren, Zhihao Wang, Guangfen Yang
The influence of features on each cluster is not the same in a mixed-type dataset. Based on the rough set and shadow set theories, the fuzzy distribution centroid was defined to represent the clustering center of the discrete feature so that the fuzzy c-means algorithm (FCM) could be extended to cluster the data with both continuous and discrete features. Then, considering the different contributions of the features to each cluster, a new weighted objective function was constructed in accordance with the principles of fuzzy compactness and separation. Because the learning feature weight is the key step in feature-weighted FCM, this paper regarded the feature weight as a variable optimized in the clustering process and put forward a self-adaptive mixed-type weighted FCM. The experimental results showed that the algorithm could be effectively applied to a heterogeneous mixed-type dataset.
{"title":"A Self-Adaptive Weighted Fuzzy c-Means for Mixed-Type Data","authors":"Min Ren, Zhihao Wang, Guangfen Yang","doi":"10.1142/s1469026820500303","DOIUrl":"https://doi.org/10.1142/s1469026820500303","url":null,"abstract":"The influence of features on each cluster is not the same in a mixed-type dataset. Based on the rough set and shadow set theories, the fuzzy distribution centroid was defined to represent the clustering center of the discrete feature so that the fuzzy c-means algorithm (FCM) could be extended to cluster the data with both continuous and discrete features. Then, considering the different contributions of the features to each cluster, a new weighted objective function was constructed in accordance with the principles of fuzzy compactness and separation. Because the learning feature weight is the key step in feature-weighted FCM, this paper regarded the feature weight as a variable optimized in the clustering process and put forward a self-adaptive mixed-type weighted FCM. The experimental results showed that the algorithm could be effectively applied to a heterogeneous mixed-type dataset.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"15 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115507127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1469026820500297
Jianzhong Shi
Bed temperature in dense-phase zone is the key parameter of circulating fluidized bed (CFB) boiler for stable combustion and economic operation. It is difficult to establish an accurate bed temperature model as the complexity of circulating fluidized bed combustion system. T-S fuzzy model was widely applied in the system identification for it can approximate complex nonlinear system with high accuracy. Fuzzy c-regression model (FCRM) clustering based on hyper-plane-shaped distance has the advantages in describing T-S fuzzy model, and Gaussian function was adapted in antecedent membership function of T-S fuzzy model. However, Gaussian fuzzy membership function was more suitable for clustering algorithm using point to point distance, such as fuzzy c-means (FCM). In this paper, a hyper-plane-shaped FCRM clustering algorithm for T-S fuzzy model identification algorithm is proposed. The antecedent membership function of proposed identification algorithm is defined by a hyper-plane-shaped membership function and an improved fuzzy partition method is applied. To illustrate the efficiency of the proposed identification algorithm, the algorithm is applied in four nonlinear systems which shows higher identification accuracy and simplified identification process. At last, the algorithm is used in a circulating fluidized bed boiler bed temperature identification process, and gets better identification result.
{"title":"Identification of Circulating Fluidized Bed Boiler Bed Temperature Based on Hyper-Plane-Shaped Fuzzy C-Regression Model","authors":"Jianzhong Shi","doi":"10.1142/s1469026820500297","DOIUrl":"https://doi.org/10.1142/s1469026820500297","url":null,"abstract":"Bed temperature in dense-phase zone is the key parameter of circulating fluidized bed (CFB) boiler for stable combustion and economic operation. It is difficult to establish an accurate bed temperature model as the complexity of circulating fluidized bed combustion system. T-S fuzzy model was widely applied in the system identification for it can approximate complex nonlinear system with high accuracy. Fuzzy c-regression model (FCRM) clustering based on hyper-plane-shaped distance has the advantages in describing T-S fuzzy model, and Gaussian function was adapted in antecedent membership function of T-S fuzzy model. However, Gaussian fuzzy membership function was more suitable for clustering algorithm using point to point distance, such as fuzzy c-means (FCM). In this paper, a hyper-plane-shaped FCRM clustering algorithm for T-S fuzzy model identification algorithm is proposed. The antecedent membership function of proposed identification algorithm is defined by a hyper-plane-shaped membership function and an improved fuzzy partition method is applied. To illustrate the efficiency of the proposed identification algorithm, the algorithm is applied in four nonlinear systems which shows higher identification accuracy and simplified identification process. At last, the algorithm is used in a circulating fluidized bed boiler bed temperature identification process, and gets better identification result.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130231965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1469026820500327
E. Jaya, B. T. Krishna
Target detection is one of the important subfields in the research of Synthetic Aperture Radar (SAR). It faces several challenges, due to the stationary objects, leading to the presence of scatter signal. Many researchers have succeeded on target detection, and this work introduces an approach for moving target detection in SAR. The newly developed scheme named Adaptive Particle Fuzzy System for Moving Target Detection (APFS-MTD) as the scheme utilizes the particle swarm optimization (PSO), adaptive, and fuzzy linguistic rules in APFS for identifying the target location. Initially, the received signals from the SAR are fed through the Generalized Radon-Fourier Transform (GRFT), Fractional Fourier Transform (FrFT), and matched filter to calculate the correlation using Ambiguity Function (AF). Then, the location of target is identified in the search space and is forwarded to the proposed APFS. The proposed APFS is the modification of standard Adaptive genetic fuzzy system using PSO. The performance of the MTD based on APFS is evaluated based on detection time, missed target rate, and Mean Square Error (MSE). The developed method achieves the minimal detection time of 4.13[Formula: see text]s, minimal MSE of 677.19, and the minimal moving target rate of 0.145, respectively.
{"title":"A Particle Fuzzy Decisive Framework for Moving Target Detection in the Multichannel SAR Framework","authors":"E. Jaya, B. T. Krishna","doi":"10.1142/s1469026820500327","DOIUrl":"https://doi.org/10.1142/s1469026820500327","url":null,"abstract":"Target detection is one of the important subfields in the research of Synthetic Aperture Radar (SAR). It faces several challenges, due to the stationary objects, leading to the presence of scatter signal. Many researchers have succeeded on target detection, and this work introduces an approach for moving target detection in SAR. The newly developed scheme named Adaptive Particle Fuzzy System for Moving Target Detection (APFS-MTD) as the scheme utilizes the particle swarm optimization (PSO), adaptive, and fuzzy linguistic rules in APFS for identifying the target location. Initially, the received signals from the SAR are fed through the Generalized Radon-Fourier Transform (GRFT), Fractional Fourier Transform (FrFT), and matched filter to calculate the correlation using Ambiguity Function (AF). Then, the location of target is identified in the search space and is forwarded to the proposed APFS. The proposed APFS is the modification of standard Adaptive genetic fuzzy system using PSO. The performance of the MTD based on APFS is evaluated based on detection time, missed target rate, and Mean Square Error (MSE). The developed method achieves the minimal detection time of 4.13[Formula: see text]s, minimal MSE of 677.19, and the minimal moving target rate of 0.145, respectively.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127295594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1469026820500261
Tingsong Ma, Wenhong Tian
Recently, a method called Meta-SR has solved the problem of super-resolution of arbitrary scale factor with only one single model. However, it has a limited reconstruction accuracy compared with RDN[Formula: see text] and EDSR[Formula: see text]. Inspired by Meta-SR, we noticed that by combining the core idea of Meta-SR and D-DBPN, we might construct a network that has as good image reconstruction accuracy as D-DBPN’s, at the same time, keeps arbitrary scaling function. According to Meta-SR’s Meta-Upscale Module, we designed a different structure called Meta-Downscale Module. By using these two different modules and back-projection structure, we construct an arbitrary back-projection network, which has the ability to enlarge images with arbitrary scale factor by using only one single model, meanwhile, obtains state-of-the-art reconstruction results. Through extensive experiments, our proposed method performs better reconstruction effect than Meta-SR and more efficient than D-DBPN. Besides that, we also evaluated the proposed method on widely used benchmark dataset on single image super-resolution. The experimental results show the superiority of our model compared to RDN+ and EDSR+.
{"title":"Arbitrary Back-Projection Networks for Image Super-Resolution","authors":"Tingsong Ma, Wenhong Tian","doi":"10.1142/s1469026820500261","DOIUrl":"https://doi.org/10.1142/s1469026820500261","url":null,"abstract":"Recently, a method called Meta-SR has solved the problem of super-resolution of arbitrary scale factor with only one single model. However, it has a limited reconstruction accuracy compared with RDN[Formula: see text] and EDSR[Formula: see text]. Inspired by Meta-SR, we noticed that by combining the core idea of Meta-SR and D-DBPN, we might construct a network that has as good image reconstruction accuracy as D-DBPN’s, at the same time, keeps arbitrary scaling function. According to Meta-SR’s Meta-Upscale Module, we designed a different structure called Meta-Downscale Module. By using these two different modules and back-projection structure, we construct an arbitrary back-projection network, which has the ability to enlarge images with arbitrary scale factor by using only one single model, meanwhile, obtains state-of-the-art reconstruction results. Through extensive experiments, our proposed method performs better reconstruction effect than Meta-SR and more efficient than D-DBPN. Besides that, we also evaluated the proposed method on widely used benchmark dataset on single image super-resolution. The experimental results show the superiority of our model compared to RDN+ and EDSR+.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132463276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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