Pub Date : 2021-04-01DOI: 10.4018/ijcini.20210401.oa6
C. Kumar, Radhika Shivhare, A. Abraham, Jinhai Li, Annapurna Jonnalagadda
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).
{"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":null,"pages":null},"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}
Pub Date : 2021-04-01DOI: 10.4018/ijcini.20210401.oa2
Liping Yang, Bin Yang, Xiaohua Gu
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.
{"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":null,"pages":null},"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}
Pub Date : 2021-04-01DOI: 10.4018/ijcini.20210401.oa9
P. Singh, Nitin Singh, R. Negi
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.
{"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":null,"pages":null},"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}
Pub Date : 2021-04-01DOI: 10.4018/ijcini.20210401.oa8
Amina Merzoug, Nacéra Benamrane, A. Taleb-Ahmed
This paper presents a segmentation method to detect multiple sclerosis (MS) lesions in brain MRI based on the artificial immune systems (AIS) and a support vector machines (SVM). In the first step, AIS is used to segment the three main brain tissues white matter, gray matter, and cerebrospinal fluid. Then the features were extracted and SVM is applied to detect the multiple sclerosis lesions based on SMO training algorithm. The experiments conducted on 3D brain MR images produce satisfying results. KeywoRDS 3D Brain MRI, AIS, Detection, Lesions, Multiple Sclerosis, Segmentation, SMO, SVM INTRoDUCTIoN Multiple sclerosis is an autoimmune chronic disease of the central nervous system especially the brain, the optic nerves and the spinal cord. The symptoms are very variable, numbness of a limb, blurred vision, loss of equilibrium...etc (Xavier et al, 2012). Magnetic resonance (MR) imaging can accurately visualize and locate plaques in both the brain and spinal cord. Depending on the sequences used, they appear white (in technical terms, we speak of “hypersignals”) or black (“hyposignals”). In 2019, more than 2.4 million people suffer from multiple sclerosis .The research is focused on finding innovative treatments to relieve people with MS. The goal of this study is to detect abnormalities of gray matter and white matter in MS from 3D RM Image Many methods have been proposed to automatically segment lesions since manual segmentation requires expert knowledge, is time consuming and is subject to intraand interexpert variability (Vera-Olmos et al, 2016). Veronese et al (Veronese et al, 2013) proposed a fuzzy classification algorithm that uses spatial information for MS lesion segmentation. In addition to spatial information, standard deviation dependent filtering is incorporated into the algorithm to provide better noise immunity. Also, fuzzy logic is adjusted to be more selective on vertical elliptical objects instead of circular objects since most plates are in this form. Saba et al (Saba et al, 2018) presented a method of segmentation of MS lesions beginning with contour detection using the canny algorithm, and then a modified blurred mean c algorithm is applied International Journal of Cognitive Informatics and Natural Intelligence Volume 15 • Issue 2 • April-June 2021 98 to increase the accuracy of the diagnosis. Pre-treatment techniques are applied to get the best result were used, such as the brain extraction tool and binarisation Bassem (Bassem, 2012) proposed a technique for segmentation of Sclerosis lesions by using texture textural features and support vector machines. They used two generic configurable components: a central processing module that locates areas of the brain that may form MS lesions, and a postprocessing module that adds or removes these areas for more accurate data. Based on these configurable modules, single-view segmentation and multiple-section view pipelines are provided to address the limitations found in segmentation r
该方法分为三个步骤(见图1)。对于每张3D MR图像，使用AIS对脑白质、灰质和脑脊液三种主要组织进行分割。作者计算了许多特征，然后将支持向量机用于MS病变分割，因为MS病变位于白质上。研究人员首先利用AIS算法对上述三种类别(灰质、白质、脑脊液)的脑MR图像进行了分割。人工免疫系统是一个包含数学和生物学原理的模型，因为自然免疫系统提供了有趣的特征，如记忆和学习，这将有助于解决问题(Tavana et al, 2016)。International Journal of Cognitive Informatics and Natural Intelligence卷15•第2期•2021年4月- 6月99 Learning此阶段是寻找代表本研究区域的记忆细胞(体素)，然后使用CLONCLAS算法使用人工克隆选择原理进行分类。该算法中使用的元素有(Komaki等人，2016):•抗体:或样本代表识别抗原图像的训练基础。•抗原:代表我们想要确定类的例子的基础。•亲和力:免疫系统中的亲和力是抗体和抗原之间相似性的度量:后两者用一个点表示。亲和是这两点之间的距离，在这个作品中，我们使用了欧几里得距离。•记忆细胞:代表为该类找到的最佳抗体。通过CLONAG算法逐类学习(Komaki et al, 2016): 1。训练样本被认为是抗体(Ab)的先验。其中一个随机抽取的样本被比作抗原(Ag)。通过欧几里得距离，他们计算出该Ag与该类所有ab的亲和力。2. Abs体素根据它们与所考虑的Ag的亲和度按降序排列。选择前N个体素进行克隆，同时保留第一个体素形成记忆细胞Mc类匹配。图1所示。国际认知信息学与自然智能杂志第15卷第2期2021年4月- 6月克隆n个选定的体素i，按其亲和度的比例。一个体素的克隆数甚至比该体素的亲和度高。这个数的计算方法如下:每个成员的克隆数:Nc = round (β* (n/I))(1)其中Nc为一个元素的克隆数，β为克隆系数，I为要克隆的元素的位置，round是将实数舍入为整数的函数。克隆总数(Erik et al, 2012):
{"title":"Lesions Detection of Multiple Sclerosis in 3D Brian MR Images by Using Artificial Immune Systems and Support Vector Machines","authors":"Amina Merzoug, Nacéra Benamrane, A. Taleb-Ahmed","doi":"10.4018/ijcini.20210401.oa8","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa8","url":null,"abstract":"This paper presents a segmentation method to detect multiple sclerosis (MS) lesions in brain MRI based on the artificial immune systems (AIS) and a support vector machines (SVM). In the first step, AIS is used to segment the three main brain tissues white matter, gray matter, and cerebrospinal fluid. Then the features were extracted and SVM is applied to detect the multiple sclerosis lesions based on SMO training algorithm. The experiments conducted on 3D brain MR images produce satisfying results. KeywoRDS 3D Brain MRI, AIS, Detection, Lesions, Multiple Sclerosis, Segmentation, SMO, SVM INTRoDUCTIoN Multiple sclerosis is an autoimmune chronic disease of the central nervous system especially the brain, the optic nerves and the spinal cord. The symptoms are very variable, numbness of a limb, blurred vision, loss of equilibrium...etc (Xavier et al, 2012). Magnetic resonance (MR) imaging can accurately visualize and locate plaques in both the brain and spinal cord. Depending on the sequences used, they appear white (in technical terms, we speak of “hypersignals”) or black (“hyposignals”). In 2019, more than 2.4 million people suffer from multiple sclerosis .The research is focused on finding innovative treatments to relieve people with MS. The goal of this study is to detect abnormalities of gray matter and white matter in MS from 3D RM Image Many methods have been proposed to automatically segment lesions since manual segmentation requires expert knowledge, is time consuming and is subject to intraand interexpert variability (Vera-Olmos et al, 2016). Veronese et al (Veronese et al, 2013) proposed a fuzzy classification algorithm that uses spatial information for MS lesion segmentation. In addition to spatial information, standard deviation dependent filtering is incorporated into the algorithm to provide better noise immunity. Also, fuzzy logic is adjusted to be more selective on vertical elliptical objects instead of circular objects since most plates are in this form. Saba et al (Saba et al, 2018) presented a method of segmentation of MS lesions beginning with contour detection using the canny algorithm, and then a modified blurred mean c algorithm is applied International Journal of Cognitive Informatics and Natural Intelligence Volume 15 • Issue 2 • April-June 2021 98 to increase the accuracy of the diagnosis. Pre-treatment techniques are applied to get the best result were used, such as the brain extraction tool and binarisation Bassem (Bassem, 2012) proposed a technique for segmentation of Sclerosis lesions by using texture textural features and support vector machines. They used two generic configurable components: a central processing module that locates areas of the brain that may form MS lesions, and a postprocessing module that adds or removes these areas for more accurate data. Based on these configurable modules, single-view segmentation and multiple-section view pipelines are provided to address the limitations found in segmentation r","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76040338","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 : 2021-04-01DOI: 10.4018/ijcini.20210401.oa1
P. Huang, Jinliang Lu
A substantial body of research has been devoted to the analysis of motion trajectories. Usually, a motion trajectory consists of a set of coordinates, which is called a raw trajectory. In this paper, the authors first use vectors for some artificially constructed global features, such as the mean discrete curvature and standard deviation of acceleration, to represent the raw trajectory data, and then apply a multiset canonical correlation analysis method to extract latent features from the artificially constructed features. The performance of the latent features is then measured by evaluating the accuracy and F1 score of a gradient boosting decision tree model for different datasets, which include paired sample datasets and unpaired sample datasets. The experimental results show that the classifier performance for MCCA features is much better than that obtained for the artificially constructed features, such as that for the motion distance or mean velocity.
{"title":"Learning Trajectory Patterns via Canonical Correlation Analysis","authors":"P. Huang, Jinliang Lu","doi":"10.4018/ijcini.20210401.oa1","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa1","url":null,"abstract":"A substantial body of research has been devoted to the analysis of motion trajectories. Usually, a motion trajectory consists of a set of coordinates, which is called a raw trajectory. In this paper, the authors first use vectors for some artificially constructed global features, such as the mean discrete curvature and standard deviation of acceleration, to represent the raw trajectory data, and then apply a multiset canonical correlation analysis method to extract latent features from the artificially constructed features. The performance of the latent features is then measured by evaluating the accuracy and F1 score of a gradient boosting decision tree model for different datasets, which include paired sample datasets and unpaired sample datasets. The experimental results show that the classifier performance for MCCA features is much better than that obtained for the artificially constructed features, such as that for the motion distance or mean velocity.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79919082","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 : 2021-04-01DOI: 10.4018/ijcini.20210401.oa4
Denis Kiselev
This paper describes an AI that uses construction grammar (CG)—a means of knowledge representation for deep understanding of text. The proposed improvements aim at more versatility of the text form and meaning knowledge structure, as well as for intelligent choosing among possible parses. Along with the improvements, computational CG techniques that form the implementation basis are explained. Evaluation experiments utilize a Winograd schema (WS)—a major test for AI—dataset and compare the implementation with state-of-the-art ones. Results have demonstrated that compared with such techniques as deep learning, the proposed CG approach has a higher potential for the task of anaphora resolution involving deep understanding of the natural language.
{"title":"An AI Using Construction Grammar to Understand Text: Parsing Improvements","authors":"Denis Kiselev","doi":"10.4018/ijcini.20210401.oa4","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa4","url":null,"abstract":"This paper describes an AI that uses construction grammar (CG)—a means of knowledge representation for deep understanding of text. The proposed improvements aim at more versatility of the text form and meaning knowledge structure, as well as for intelligent choosing among possible parses. Along with the improvements, computational CG techniques that form the implementation basis are explained. Evaluation experiments utilize a Winograd schema (WS)—a major test for AI—dataset and compare the implementation with state-of-the-art ones. Results have demonstrated that compared with such techniques as deep learning, the proposed CG approach has a higher potential for the task of anaphora resolution involving deep understanding of the natural language.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83917740","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 : 2021-04-01DOI: 10.4018/ijcini.20210401.oa5
Chang-Ai Chen, Min Chen
Nowadays the number of college students' suicides are increasing for the insufficient social support or poor interpersonal relations. Furthermore, not much attention has been concerned to students' interpersonal relations when handling student affairs and only very limited information about students' interaction network is available. This paper studies the peer network of college students by using the tool of social network analysis. And it aims to serve as instrumental support for students to foster and develop harmonious interpersonal relations. It offers new information for school counsellor to better handle student affairs and provides information support for the carrying out of moral and ideological guidance for students.
{"title":"The Research of Social Network Analysis on College Students' Interactive Relations","authors":"Chang-Ai Chen, Min Chen","doi":"10.4018/ijcini.20210401.oa5","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa5","url":null,"abstract":"Nowadays the number of college students' suicides are increasing for the insufficient social support or poor interpersonal relations. Furthermore, not much attention has been concerned to students' interpersonal relations when handling student affairs and only very limited information about students' interaction network is available. This paper studies the peer network of college students by using the tool of social network analysis. And it aims to serve as instrumental support for students to foster and develop harmonious interpersonal relations. It offers new information for school counsellor to better handle student affairs and provides information support for the carrying out of moral and ideological guidance for students.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81345396","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 : 2021-04-01DOI: 10.4018/ijcini.20210401.oa3
Fei Tang
To improve the performance of bionic algorithms, an intelligent bionic optimization algorithm is proposed based on the morphological characteristics of trees growing toward light. The growth organ of the tree is mapped into the coding of the tree growth algorithm, and the entire tree is formed by selecting the fastest growing individual to form the next level of the tree. When the tree growth reaches a certain level, the individual code of the shoot tip is added to enhance the search ability of the individual shoot tip in the growth space of the entire tree. This method achieves a near-optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function. The experimental results show that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm or the ant colony algorithm.
{"title":"Intelligent Bionic Optimization Algorithm Based on the Growth Characteristics of Tree Branches","authors":"Fei Tang","doi":"10.4018/ijcini.20210401.oa3","DOIUrl":"https://doi.org/10.4018/ijcini.20210401.oa3","url":null,"abstract":"To improve the performance of bionic algorithms, an intelligent bionic optimization algorithm is proposed based on the morphological characteristics of trees growing toward light. The growth organ of the tree is mapped into the coding of the tree growth algorithm, and the entire tree is formed by selecting the fastest growing individual to form the next level of the tree. When the tree growth reaches a certain level, the individual code of the shoot tip is added to enhance the search ability of the individual shoot tip in the growth space of the entire tree. This method achieves a near-optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function. The experimental results show that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm or the ant colony algorithm.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85344051","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 : 2021-01-01DOI: 10.4018/ijcini.2021010104
Saumendra Kumar Mohapatra, M. Mohanty
In this piece of work, the authors have attempted to classify four types of long duration arrhythmia electrocardiograms (ECG) using radial basis function network (RBFN). The data is taken from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, and features are extracted using empirical mode decomposition (EMD) technique. For most informative contents average power (AP) and coefficient of dispersion (CD) are evaluated from six intrinsic mode function (IMFs) of EMD. Principal component analysis (PCA) is used for feature reduction for effective classification using RBFN. The performance is shown in the result section, and it is found that the classification accuracy is 95.98%.
{"title":"Arrhythmia Classification Using Radial Basis Function Network With Selective Features From Empirical Mode Decomposition","authors":"Saumendra Kumar Mohapatra, M. Mohanty","doi":"10.4018/ijcini.2021010104","DOIUrl":"https://doi.org/10.4018/ijcini.2021010104","url":null,"abstract":"In this piece of work, the authors have attempted to classify four types of long duration arrhythmia electrocardiograms (ECG) using radial basis function network (RBFN). The data is taken from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, and features are extracted using empirical mode decomposition (EMD) technique. For most informative contents average power (AP) and coefficient of dispersion (CD) are evaluated from six intrinsic mode function (IMFs) of EMD. Principal component analysis (PCA) is used for feature reduction for effective classification using RBFN. The performance is shown in the result section, and it is found that the classification accuracy is 95.98%.","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76929873","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 : 2021-01-01DOI: 10.4018/ijcini.20211001.oa15
Na Li, Zhenghe Yang
{"title":"Brain Tumor Segmentation From Multimodal MRI Data Based on GLCM and SVM Classifier","authors":"Na Li, Zhenghe Yang","doi":"10.4018/ijcini.20211001.oa15","DOIUrl":"https://doi.org/10.4018/ijcini.20211001.oa15","url":null,"abstract":"","PeriodicalId":43637,"journal":{"name":"International Journal of Cognitive Informatics and Natural Intelligence","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77555290","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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