Int. J. Comput. Intell. Appl.最新文献_第7页

RFCBF: enhance the performance and stability of Fast Correlation-Based Filter RFCBF:提高快速相关滤波器的性能和稳定性

Int. J. Comput. Intell. Appl. Pub Date : 2021-05-30 DOI: 10.1142/s1469026822500092

Xiongshi Deng, Min Li, Lei Wang, Qikang Wan

引用次数: 3

Decision Support System Based on Fuzzy Logic for Assessment of Expected Corporate Income Performance 基于模糊逻辑的企业预期收益绩效评价决策支持系统

Int. J. Comput. Intell. Appl. Pub Date : 2021-05-19 DOI: 10.1142/S1469026821500097

A. Yosef, E. Shnaider, Rimona Palas, Amos Baranes

引用次数: 1

Stretch Sensor-Based Facial Expression Recognition and Classification Using Machine Learning 基于拉伸传感器的面部表情识别与分类

Int. J. Comput. Intell. Appl. Pub Date : 2021-04-06 DOI: 10.1142/S1469026821500103

C. M. M. Refat, N. Azlan

引用次数: 0

Hybrid Fuzzy-Genetic Model for Fitness-Based Performance Optimization in Wireless Networks 基于适应度的无线网络性能优化的混合模糊遗传模型

Int. J. Comput. Intell. Appl. Pub Date : 2021-01-22 DOI: 10.1142/S1469026821500085

R. Mehta

引用次数: 1

An Enhanced Sentiment Analysis Framework Based on Pre-Trained Word Embedding 基于预训练词嵌入的增强情感分析框架

Int. J. Comput. Intell. Appl. Pub Date : 2020-12-01 DOI: 10.1142/s1469026820500315

E. Mohamed, M. Moussa, M. Haggag

{"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}

引用次数: 8

A Novel Method to Identify Golgi Protein Types Based on Hybrid Feature and SVM Algorithm 一种基于混合特征和支持向量机算法的高尔基蛋白类型识别方法

Int. J. Comput. Intell. Appl. Pub Date : 2020-12-01 DOI: 10.1142/s1469026820500273

Liang Ma, Hailin Jiang, Wanli Yang, Quanjie Zhu

引用次数: 1

A Self-Adaptive Weighted Fuzzy c-Means for Mixed-Type Data 混合类型数据的自适应加权模糊c均值

Int. J. Comput. Intell. Appl. Pub Date : 2020-12-01 DOI: 10.1142/s1469026820500303

Min Ren, Zhihao Wang, Guangfen Yang

引用次数: 1

Identification of Circulating Fluidized Bed Boiler Bed Temperature Based on Hyper-Plane-Shaped Fuzzy C-Regression Model 基于超平面模糊c回归模型的循环流化床锅炉床温辨识

Int. J. Comput. Intell. Appl. Pub Date : 2020-12-01 DOI: 10.1142/s1469026820500297

Jianzhong Shi

{"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}

引用次数: 1

A Particle Fuzzy Decisive Framework for Moving Target Detection in the Multichannel SAR Framework 多通道SAR框架下运动目标检测的粒子模糊决策框架

Int. J. Comput. Intell. Appl. Pub Date : 2020-12-01 DOI: 10.1142/s1469026820500327

E. Jaya, B. T. Krishna

{"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}

引用次数: 3

Arbitrary Back-Projection Networks for Image Super-Resolution 用于图像超分辨率的任意反投影网络

Int. J. Comput. Intell. Appl. Pub Date : 2020-12-01 DOI: 10.1142/s1469026820500261

Tingsong Ma, Wenhong Tian

{"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}

引用次数: 1