Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.     

基于主题图的公安案件信息资源知识体系研究

文章探讨了将主题图技术应用于公安案件的信息资源组织。通过对主题图技术的概念和理论的介绍,建立了公安案件主题图概念体系。结合实际的公安案件,分析了公安案件中主题及主题间的关联。运用OKS知识开发组件工具进行了具体的构建,最终达到了用Omnigator实现基于语义的主题图浏览,Vizigator实现主题图可视化。     

Rolling element bearing fault diagnosis using wavelet transform

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.     

Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation

This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter.     

Learning the attachment theory with the CM-ED concept map editor

This paper presents a study carried out at the University of the Basque Country UPV/EHU with the aim of evaluating the CM-ED (concept map editor) with social education students. Concept mapping is a widely accepted technique that promotes meaningful learning. Graphically representing concepts of the learning domain and relationships between them helps students integrate new knowledge into their current cognitive structure. Due to the flexibility of computer-aided drawing graphs, several concept mapping tools have been developed and their use has been studied over the last few years. CM-ED is a multilingual and multimedia software program designed for drawing concept maps. Until recently, CM-ED had been mainly used and evaluated in computer science university degree. This paper represents a qualitative step in the evaluation of CM-ED: from technical students to students of more theoretical fields. The main characteristics of the CM-ED editor and the carried out study are presented in this paper.     

Computational intelligence tools for next generation quality of service management

In this paper we explore the interest of computational intelligence tools in the management of heterogeneous communication networks, specifically to predict congestion, failures and other anomalies in the network that may eventually lead to degradation of the quality of offered services. We show two different applications based on neural and neuro-fuzzy systems for quality of service (QoS) management in next generation networks for voice and video service over heterogeneous Internet protocol (V2oIP) services. The two examples explained in this paper attempt to predict the communication network resources for new incoming calls, and visualizing the QoS of a communication network by means of self-organizing maps.     

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

基于Guided Filter的地形图中线要素提取算法

针对彩色扫描地形图中线要素与背景要素难以分离,现有线要素提取算法提取的结果中存在边界不准确的问题,提出基于Guided Filter的地形图中线要素提取算法。利用基于能量密度和Shear变换相结合的线要素提取算法对线要素进行粗提取;引入Guided Filter,以源地形图图像作为Guided Filter的引导图像,以粗提取的线要素图像作为Guided Filter的输入图像,经过滤波处理获得的线要素信息更为显著;利用OTSU算法得到最终的线要素信息。实验结果表明：相对于现有的线要素提取算法,提出的算法能够更为准确地提取出地形图中的线要素,并具有更好的噪声抑制能力。     

多服务器环境下基于扩展混沌映射的认证密钥协商协议

传统的单服务器环境下基于智能卡认证方案,单个服务器对所有的注册远程用户提供服务。如果用户想要从不同的服务器获得网络服务,则必须分别在不同的服务器注册。为解决以上问题,研究者提出了多服务器认证方案。然而,文献中的大部分方案都不能实现强安全特性。受到切比雪夫映射的半群特性和基于扩展混沌映射的密钥协商协议启发,本文提出一种多服务器环境中的认证方案。新方案不需要使用验证表并且允许用户访问不同的服务器而不需要分别注册。新方案不仅可以抵抗各类攻击,还实现了用户的强匿名性。和以前的相关协议相比,新协议具有高效性和安全性,因而适合在实际环境中应用。