发布求助
文献互助 智能选刊 最新文献

International Journal of Cognitive Informatics and Natural Intelligence最新文献

筛选
英文 中文
Robust Face Recognition Under Partial Occlusion Based on Local Generic Features 基于局部通用特征的局部遮挡下人脸鲁棒识别
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-07-01 DOI: 10.4018/IJCINI.20210701.OA4
Amit Kumar Yadav, Neeraj Gupta, Aamir Khan, A. S. Jalal
{"title":"Robust Face Recognition Under Partial Occlusion Based on Local Generic Features","authors":"Amit Kumar Yadav, Neeraj Gupta, Aamir Khan, A. S. Jalal","doi":"10.4018/IJCINI.20210701.OA4","DOIUrl":"https://doi.org/10.4018/IJCINI.20210701.OA4","url":null,"abstract":"Face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics, and so on. It is a challenging task in the field of computer vision. Although the various state-of-the-art methods of face recognition in constrained environments have achieved satisfactory results, there are still many issues which are untouched in unconstrained environments, such as partial occlusions, large pose variations, etc. In this paper, the authors have proposed an approach which utilized the local generic feature (LGF) to recognize the face in the partial occlusion by fusing features scale invariant feature transform (SIFT) and multi-block local binary pattern (MB-LBP). It also utilizes robust kernel method for classification of the query image. They have validated the effectiveness of the proposed approach on the benchmark AR face database. The experimental outcomes illustrate that the proposed approach outperformed the state-of-art methods for robust face recognition.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"26 1","pages":"47-57"},"PeriodicalIF":0.9,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86432332","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
A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing 移动云计算中基于多目标遗传算法的资源调度
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-07-01 DOI: 10.4018/IJCINI.20210701.OA5
S. Ramasubbareddy, Evakattu Swetha, A. Luhach, T. Srinivas
{"title":"A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing","authors":"S. Ramasubbareddy, Evakattu Swetha, A. Luhach, T. Srinivas","doi":"10.4018/IJCINI.20210701.OA5","DOIUrl":"https://doi.org/10.4018/IJCINI.20210701.OA5","url":null,"abstract":"Mobile cloud computing is an emerging technology in recent years. This technology reduces battery consumption and execution time by executing mobile applications in remote cloud server. The virtual machine (VM) load balancing among cloudlets in MCC improves the performance of application in terms of response time. Genetic algorithm (GA) is popular for providing optimal solution for load balancing problems. GA can perform well in both homogeneous and heterogeneous environments. In this paper, the authors consider multi-objective genetic algorithm for load balancing in MCC (MOGALMCC) environment. In MOGALMCC, they consider distance, bandwidth, memory, and cloudlet server load to find optimal cloudlet before scheduling VM in another cloudlet. The framework MOGALMCC aims to improve response time as well as minimizes VM failure rate. The experiment result shows that proposed model performed well by reducing execution time and task waiting time at server.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"63 1","pages":"58-73"},"PeriodicalIF":0.9,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89127633","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: 6
A Novel GUI-Based Image Reconstruction Algorithm of EIT Imaging Technique 一种新的基于gui的EIT成像技术图像重建算法
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-07-01 DOI: 10.4018/IJCINI.20210701.OA3
Ramesh Kumar, S. Tripathi
{"title":"A Novel GUI-Based Image Reconstruction Algorithm of EIT Imaging Technique","authors":"Ramesh Kumar, S. Tripathi","doi":"10.4018/IJCINI.20210701.OA3","DOIUrl":"https://doi.org/10.4018/IJCINI.20210701.OA3","url":null,"abstract":"Electrical impedance tomography (EIT) is a non-invasive technique that is used to estimate the electrical properties of a medical or non-medical object through the boundary data of the object. It used to achieve functional imaging of different objects by measuring electrical conductivity and impedance parameters. In this paper, a novel image reconstruction algorithm is presented, which is based on graphical user interface (GUI) developed on MATLAB software platform. EIT imaging algorithm consists of a forward problem and an inverse problem. The forward problem is formulated with the conductance matrix, and a non-iterative inverse method is used to estimate the conductivity distribution. Image display and data analysis are implemented and controlled directly in the GUI. The numerical simulations and phantom experiments have been carried out to evaluate the performance of the proposed algorithm and other previous research data through quantitative parameters. The obtained result shows satisfactory and comparable results to other EIT imaging algorithm.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"35 1","pages":"31-46"},"PeriodicalIF":0.9,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78768277","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
Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text 基于混合深度学习方法的印度语文本实体抽取
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-07-01 DOI: 10.4018/IJCINI.20210701.OA1
Richa Sharma, Sudha Morwal, Basant Agarwal
{"title":"Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text","authors":"Richa Sharma, Sudha Morwal, Basant Agarwal","doi":"10.4018/IJCINI.20210701.OA1","DOIUrl":"https://doi.org/10.4018/IJCINI.20210701.OA1","url":null,"abstract":"This article presents a neural network-based approach to develop named entity recognition for Hindi text. In this paper, the authors propose a deep learning architecture based on convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) neural network. Skip-gram approach of word2vec model is used in the proposed model to generate word vectors. In this research work, several deep learning models have been developed and evaluated as baseline systems such as recurrent neural network (RNN), long short-term memory (LSTM), Bi-LSTM. Furthermore, these baseline systems are promoted to a proposed model with the integration of CNN and conditional random field (CRF) layers. After a comparative analysis of results, it is verified that the performance of the proposed model (i.e., Bi-LSTM-CNN-CRF) is impressive. The proposed system achieves 61% precision, 56% recall, and 58% F-measure.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"132 1","pages":"1-11"},"PeriodicalIF":0.9,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90215404","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
Semrank
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-07-01 DOI: 10.4018/ijcini.20210701.oa6
Jagrati Singh, Anil Kumar Singh
{"title":"Semrank","authors":"Jagrati Singh, Anil Kumar Singh","doi":"10.4018/ijcini.20210701.oa6","DOIUrl":"https://doi.org/10.4018/ijcini.20210701.oa6","url":null,"abstract":"Popular real-world events often create huge traffic on Twitter including real-time updates of important moments, personal comments, and so on while the event is happening. Most of the users are interested to read the important tweets that possibly include important moments of that event. However, extracting the relevant tweets of any event is a challenging task due to the endless stream of noisy tweets and vocabulary variation problem of social media content. To handle these challenges, the authors introduce a new approach for computing the relative tweet importance based on the concept of the Pagerank algorithm where adjacency matrix of the graph representation of tweets contains semantic similarity matrix based on the word mover's distance measure utilizing Word2Vec word embedding model. The results show that top-ranked tweets generated by the proposed approach are more concise and news-worthy than baseline approaches.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"183 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80447994","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
Research on IRP of Perishable Products Based on Mobile Data Sharing Environment 基于移动数据共享环境的易腐产品IRP研究
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-04-01 DOI: 10.4018/ijcini.20210401.oa10
Zelin Wang, Xiaoning Wei, Jiansheng Pan
{"title":"Research on IRP of Perishable Products Based on Mobile Data Sharing Environment","authors":"Zelin Wang, Xiaoning Wei, Jiansheng Pan","doi":"10.4018/ijcini.20210401.oa10","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa10","url":null,"abstract":"Inventory routing problem (IRP) has always been a hot issue. Due to its particularity, perishable products have high requirements for inventory and transportation. In order to reduce the losses of perishable goods and improve the storage efficiency of perishable goods, based on the general inventory path problem, this paper further has studied the IRP of perishable goods. In addition, in the process of product distribution and transportation, there are a lot of real-time product information generated dynamically. These real-time mobile data must be shared by the whole distribution network, which will also dynamically affect the efficiency of IRP research. On the basis of some assumptions, the mathematical model has been established with inventory and vehicle as constraints and the total cost of the system as the objective. In view of the particularity of perishable inventory path problem, this paper proposed an improved differential evolution algorithm (IDE) to improve the differential evolution algorithm from two aspects. Firstly, the population has been initialized by gridding and the greedy local optimization algorithm has been used to assist the differential evolution algorithm, with these measures to improve the convergence speed of the algorithm. Then, the accuracy of the algorithm is improved by the adaptive scaling factor, two evolution modes and changing the constraints of the problem. Then the improved algorithm has been used to solve the inventory path problem. The results of numerical experiments show that the algorithm is effective and feasible and can improve the accuracy and speed up the convergence of the algorithm.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"76 1","pages":"5-23"},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90476604","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: 2
Hybrid PSO-ANFIS for Speaker Recognition 混合PSO-ANFIS用于说话人识别
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-04-01 DOI: 10.4018/ijcini.20210401.oa7
Samiya Silarbi, R. Tlemsani, A. Bendahmane
{"title":"Hybrid PSO-ANFIS for Speaker Recognition","authors":"Samiya Silarbi, R. Tlemsani, A. Bendahmane","doi":"10.4018/ijcini.20210401.oa7","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa7","url":null,"abstract":"This paper introduces an evolutionary approach for training the adaptive network-based fuzzy inference system (ANFIS). The previous works are based on gradient descendent (GD); this algorithm converges very slowly and gets stuck down at bad local minima. This study applies one of the swarm intelligent branches, named particle swarm optimization (PSO), where the premise parameters of the rules are optimized by a PSO, and the conclusion part is optimized by least-squares estimation (LSE). The hybrid PSO-ANFIS model is performed for speaker recognition on CHAINS speech dataset. The results obtained by the hybrid model showed an improvement on the accuracy compared to similar ANFIS based on gradient descendent optimization.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"2 1","pages":"96-109"},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82004584","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
Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization 基于混合自回归综合移动平均模型和动态粒子群优化的短期风电预测
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-04-01 DOI: 10.4018/ijcini.20210401.oa9
P. Singh, Nitin Singh, R. Negi
{"title":"Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization","authors":"P. Singh, Nitin Singh, R. Negi","doi":"10.4018/ijcini.20210401.oa9","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa9","url":null,"abstract":"With the upsurge in restructuring of the power markets, wind power has become one of the key factors in power generation in the smart grids and gained momentum in the recent years. The accurate wind power forecasting is highly desirable for reduction of the reserve capability, enhancement in penetration of the wind power, stability and economic operation of the power system. The time series models are extensively used for the wind power forecasting. The model estimation in the ARIMA model is usually accomplished by maximizing the log likelihood function and it requires to be re-estimated for any change in input value. This degrades the performance of the ARIMA model. In the proposed work, the model estimation of the ARIMA model is done using latest evolutionary algorithm (i.e., dynamic particle swarm optimization [DPSO]). The use of DPSO algorithm eliminates the need for re-estimation of the model coefficients for any change in input value and moreover, it improves the performance of ARIMA model. The performance of proposed DPSO-ARIMA model has been compared to the existing models.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"29 1","pages":"124-151"},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90307124","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: 10
Adversarial Reconstruction CNN for Illumination-Robust Frontal Face Image Recovery and Recognition 用于光照鲁棒正面人脸图像恢复和识别的对抗重建CNN
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-04-01 DOI: 10.4018/ijcini.20210401.oa2
Liping Yang, Bin Yang, Xiaohua Gu
{"title":"Adversarial Reconstruction CNN for Illumination-Robust Frontal Face Image Recovery and Recognition","authors":"Liping Yang, Bin Yang, Xiaohua Gu","doi":"10.4018/ijcini.20210401.oa2","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa2","url":null,"abstract":"This article proposes an adversarial reconstruction convolution neural network (ARCNN) for non-uniform illumination frontal face image recovery and recognition. The proposed ARCNN includes a reconstruction network and a discriminative network. The authors employ GAN framework to learn the reconstruction network in an adversarial manner. This article integrates gradient loss and perceptual loss terms, which are able to preserve the detailed and spatial structure image information, into the overall reconstruction loss function to constraint the reconstruction procedure. Experiments are conducted on the typical illumination-sensitive dataset, extended YaleB dataset. The reconstructed results demonstrate that the proposed ARCNN approach can remove the illumination and shadow information and recover natural uniform illuminated face image from non-uniform illuminated ones. Face recognition results on the extended YaleB dataset demonstrate that the proposed ARCNN reconstruction procedure can also preserve the discriminative information of face image for classification task.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"47 1","pages":"18-33"},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87103740","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: 7
A Pragmatic Approach to Understand Hebbian Cell Assembly 用实用的方法来理解希伯比细胞组装
IF 0.9
International Journal of Cognitive Informatics and Natural Intelligence Pub Date : 2021-04-01 DOI: 10.4018/ijcini.20210401.oa6
C. Kumar, Radhika Shivhare, A. Abraham, Jinhai Li, Annapurna Jonnalagadda
{"title":"A Pragmatic Approach to Understand Hebbian Cell Assembly","authors":"C. Kumar, Radhika Shivhare, A. Abraham, Jinhai Li, Annapurna Jonnalagadda","doi":"10.4018/ijcini.20210401.oa6","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa6","url":null,"abstract":"Formed at the cerebral cortex, neuron cell assemblies are regarded as basic units in cortical representation. Proposed by Hebb, these cell assemblies are regarded as the distributed neural representation of relevant objects, concepts or constellations. Each cell assembly contains a group of neurons having strong mutual excitatory connections. During a stimulus, these cells get activated. This activation either performs a given action or represent a given percept or concept in brain. This theory is in the strongest connection of the problem of concept forming in the brain. The challenge is to model coordinated activity among neurons in brain mathematically. The need of modelling it mathematically enables this paper to give clear view of functionality of Hebbian cell assembly. Therefore this paper proposes a pragmatic approach to Hebbian cell assemblies using mathematical model grounded in lattice based formalism that utilizes Galois connections. During this proposal, the authors also show the connections of the proposal to cognitive model of memory in particularly long-term memory (LTM).","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":"8 1","pages":"73-95"},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84697934","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信