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International Journal of Cognitive Informatics and Natural Intelligence最新文献

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Linguistic Processor Integration for Solving Planimetric Problems 求解平面问题的语言处理器集成
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/IJCINI.20211001.OA37
S. Kurbatov
{"title":"Linguistic Processor Integration for Solving Planimetric Problems","authors":"S. Kurbatov","doi":"10.4018/IJCINI.20211001.OA37","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA37","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"164 1","pages":"1-14"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86442838","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: 0
An ACO-Based Clustering Algorithm With Chaotic Function Mapping 一种基于aco的混沌函数映射聚类算法
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/ijcini.20211001.oa20
Lei Yang, Xin Hu, Hui Wang, Wensheng Zhang, K. Huang, Dongya Wang
{"title":"An ACO-Based Clustering Algorithm With Chaotic Function Mapping","authors":"Lei Yang, Xin Hu, Hui Wang, Wensheng Zhang, K. Huang, Dongya Wang","doi":"10.4018/ijcini.20211001.oa20","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa20","url":null,"abstract":"To overcome shortcomings when the ant colony optimization clustering algorithm (ACOC) deal with the clustering problem, this paper introduces a novel ant colony optimization clustering algorithm with chaos. The main idea of the algorithm is to apply the chaotic mapping function in the two stages of ant colony optimization: pheromone initialization and pheromone update. The application of chaotic mapping function in the pheromone initialization phase can encourage ants to be distributed in as many different initial states as possible. Applying the chaotic mapping function in the pheromone update stage can add disturbance factors to the algorithm, prompting the ants to explore new paths more, avoiding premature convergence and premature convergence to suboptimal solutions. Extensive experiments on the traditional and proposed algorithms on four widely used benchmarks are conducted to investigate the performance of the new algorithm. These experiments results demonstrate the competitive efficiency, effectiveness, and stability of the proposed algorithm.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"67 1","pages":"1-21"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82959788","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: 0
Efficient Traffic Sign Recognition Using CLAHE-Based Image Enhancement and ResNet CNN Architectures 基于clahe图像增强和ResNet CNN架构的高效交通标志识别
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/ijcini.295811
Utkarsh Dubey, R. Chaurasiya
{"title":"Efficient Traffic Sign Recognition Using CLAHE-Based Image Enhancement and ResNet CNN Architectures","authors":"Utkarsh Dubey, R. Chaurasiya","doi":"10.4018/ijcini.295811","DOIUrl":"https://doi.org/10.4018/ijcini.295811","url":null,"abstract":"Recognition and classification of traffic signs and other numerous displays on the road are very crucial for autonomous driving, navigation, and safety systems on roads. Machine learning or deep learning methods are generally employed to develop a traffic sign recognition (TSR) system. This paper proposes a novel two-step TSR approach consisting of contrast limited adaptive histogram equalization (CLAHE)-based image enhancement and convolutional neural network (CNN) as multiclass classifier. Three CNN architectures viz. LeNet, VggNet, and ResNet were employed for classification. All the methods were tested for classification of German traffic sign recognition benchmark (GTSRB) dataset. The experimental results presented in the paper endorse the capability of the proposed work. Based on experimental results, it has also been illustrated that the proposed novel architecture consisting of CLAHE-based image enhancement & ResNet-based classifier has helped to obtain better classification accuracy as compared to other similar approaches.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"42 1","pages":"1-19"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77640137","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: 4
Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR 利用DT和VAR优化物联网物理位置监控
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/IJCINI.287597
A. S. Shitole, M. Devare
{"title":"Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR","authors":"A. S. Shitole, M. Devare","doi":"10.4018/IJCINI.287597","DOIUrl":"https://doi.org/10.4018/IJCINI.287597","url":null,"abstract":"This study shows an enhancement of IoT which gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDaddy Cloud, whenever the camera detects a person to optimize the Physical Location Monitoring System by reducing the bandwidth requirement and storage cost onto the Cloud using edge computation. The study reveals that Decision Tree (DT) and Random Forest give reasonably similar macro average f1-score to predict a person using sensor data. Experimental results show that DT is the most reliable predictive model for the Cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate time series prediction using Vector Auto Regression that gives reasonably good Root Mean Squared Error to predict Temperature, Humidity, Light Dependent Resistor, and Gas time series.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"26 1","pages":"1-28"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75789365","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
Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO 基于CKMOPSO的柑橘黄龙冰识别算法
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/IJCINI.20211001.OA10
Hui Wang, Tie Cai, Wei-fang Cao
{"title":"Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO","authors":"Hui Wang, Tie Cai, Wei-fang Cao","doi":"10.4018/IJCINI.20211001.OA10","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA10","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"26 1","pages":"1-11"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91029984","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
Object Tracking Based on Global Context Attention 基于全局上下文关注的目标跟踪
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/ijcini.287595
Yucheng Wang, Xi Chen, Zhongjie Mao, Jia Yan
{"title":"Object Tracking Based on Global Context Attention","authors":"Yucheng Wang, Xi Chen, Zhongjie Mao, Jia Yan","doi":"10.4018/ijcini.287595","DOIUrl":"https://doi.org/10.4018/ijcini.287595","url":null,"abstract":"Previous research has shown that tracking algorithms cannot capture long-distance information and lead to the loss of the object when the object was deformed, the illumination changed, and the background was disturbed by similar objects. To remedy this, this article proposes an object-tracking method by introducing the Global Context attention module into the Multi-Domain Network (MDNet) tracker. This method can learn the robust feature representation of the object through the Global Context attention module to better distinguish the background from the object in the presence of interference factors. Extensive experiments on OTB2013, OTB2015, and UAV20L datasets show that the proposed method is significantly improved compared with MDNet and has competitive performance compared with more mainstream tracking algorithms. At the same time, the method proposed in this article achieves better results when the video sequence contains object deformation, illumination change, and background interference with similar objects.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"7 1","pages":"1-16"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81991681","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: 0
Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN 基于三维卷积神经网络的脑肿瘤MRI图像分割
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/ijcini.20211001.oa4
A. Kharrat, M. Neji
{"title":"Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN","authors":"A. Kharrat, M. Neji","doi":"10.4018/ijcini.20211001.oa4","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa4","url":null,"abstract":"We consider the problem of fully automatic brain tumor segmentation in MR images containing glioblastomas. We propose a three Dimensional Convolutional Neural Network (3D MedImg-CNN) approach which achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D MedImg-CNN is formed directly on the raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascaded architecture with two pathways that each model normal details in tumors. Fully exploiting the convolutional nature of our model also allows us to segment a complete cerebral image in one minute. The performance of the proposed 3D MedImg-CNN with CNN segmentation method is computed using dice similarity coefficient (DSC). In experiments on the 2013, 2015 and 2017 BraTS challenges datasets; we unveil that our approach is among the most powerful methods in the literature, while also being very effective.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"47 1","pages":"1-17"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85491377","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
An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem 中文TSP问题的一种改进的带Stud交叉的布谷鸟搜索算法
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/ijcini.20211001.oa17
Anbang Wang, Lihong Guo, Yuan Chen, Junjie Wang, Luo Liu, Yuanzhang Song
{"title":"An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem","authors":"Anbang Wang, Lihong Guo, Yuan Chen, Junjie Wang, Luo Liu, Yuanzhang Song","doi":"10.4018/ijcini.20211001.oa17","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa17","url":null,"abstract":"The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"28 1","pages":"1-26"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89273195","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: 0
Bio-Inspired Data Mining for Optimizing GPCR Function Identification 生物启发数据挖掘优化GPCR功能鉴定
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/IJCINI.20211001.OA40
Safia Bekhouche, Y. M. B. Ali
{"title":"Bio-Inspired Data Mining for Optimizing GPCR Function Identification","authors":"Safia Bekhouche, Y. M. B. Ali","doi":"10.4018/IJCINI.20211001.OA40","DOIUrl":"https://doi.org/10.4018/IJCINI.20211001.OA40","url":null,"abstract":"GPCRs are the largest family of cell surface receptors; many of them remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However, the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy [FES], data mining algorithm [DMA]). The authors propose to use the BAT algorithm for extracting the pertinent features and the genetic algorithm to choose the best couple. They compared the results they obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"4 1","pages":"1-31"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80953921","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: 0
Research of Image Recognition of Plant Diseases and Pests Based on Deep Learning 基于深度学习的植物病虫害图像识别研究
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-10-01 DOI: 10.4018/ijcini.295810
W. Feng, Huang Xue Hua
{"title":"Research of Image Recognition of Plant Diseases and Pests Based on Deep Learning","authors":"W. Feng, Huang Xue Hua","doi":"10.4018/ijcini.295810","DOIUrl":"https://doi.org/10.4018/ijcini.295810","url":null,"abstract":"Deep learning has attracted more and more attention in speech recognition, visual recognition and other fields. In the field of image processing, using deep learning method can obtain high recognition rate. In this paper, the convolution neural network is used as the basic model of deep learning. The shortcomings of the model are analyzed, and the DBN is used for the image recognition of diseases and insect pests. In the experiment, firstly, we select 10 kinds of disease and pest leaves and 50000 normal leaves, each of which is used for the comparison of algorithm performance.In the judgment of disease and pest species, the algorithm proposed in this study can identify all kinds of diseases and insect pests to the maximum extent, but the corresponding software (openCV, Access) recognition accuracy will gradually reduce along with the increase of the types of diseases and insect pests. In this study, the algorithm proposed in the identification of diseases and insect pests has been kept at about 45%.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"58 1","pages":"1-21"},"PeriodicalIF":0.9,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86886313","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
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