Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions.To solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization model.Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph.The DHMR and L_(1) norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods.Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

Deep learning algorithm featuring continuous learning for modulation classifications in wireless networks

Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.

Achievement of Interference Alignment in General Underlay Cognitive Radio Networks: Scenario Classification and Adaptive Spectrum Sharing

Interference alignment(IA) is suitable for cognitive radio networks(CRNs).However, in IA spectrum sharing(SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user(PU), which may lead to low signalto-noise-ratio at cognitive radio receivers(CRRs). Consequently, sum rate of cognitive users(CUs) may fall short of the theoretical maximum through IA. To solve this problem,we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU.

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

Natural Scene Classification Inspired by Visual Perception and Cognition Mechanisms

The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.

Individual identification using multi-metric of DTI in Alzheimer's disease and mild cognitive impairment

Accurate identification of Alzheimer＇s disease （AD） and mild cognitive impairment （MCI） is crucial so as to improve diagnosis techniques and to better understand the neurodegenerative process. In this work, we aim to apply the machine learning method to individual identification and identify the discriminate features associated with AD and MCI. Diffusion tensor imaging scans of 48 patients with AD, 39 patients with late MCI, 75 patients with early MCI, and 51 age-matched healthy controls （HCs） are acquired from the Alzheimer＇s Disease Neuroimaging Initiative database. In addition to the common fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity metrics, there are two novel metrics, named local diffusion homogeneity that used Spearman＇s rank correlation coefficient and Kendall＇s coefficient concordance, which are taken as classification metrics. The recursive feature elimination method for support vector machine （SVM） and logistic regression （LR） combined with leave-one-out cross validation are applied to determine the optimal feature dimensions. Then the SVM and LR methods perform the classification process and compare the classification performance. The results show that not only can the multi-type combined metrics obtain higher accuracy than the single metric, but also the SVM classifier with multi-type combined metrics has better classification performance than the LR classifier. Statistically, the average accuracy of the combined metric is more than 92% for all between-group comparisons of SVM classifier. In addition to the high recognition rate, significant differences are found in the statistical analysis of cognitive scores between groups. We further execute the permutation test, receiver operating characteristic curves, and area under the curve to validate the robustness of the classifiers, and indicate that the SVM classifier is more stable and efficient than the LR classifier. Finally, the uncinated fasciculus, cingulum, corpus callosum, corona radiate, external capsule, and internal capsule have been regarded as the most important white matter tracts to identify AD, MCI, and HC. Our findings reveal a guidance role for machine-learning based image analysis on clinical diagnosis.

Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.

Classification of Attribute Mastery Patterns Using Deep Learning

It is very important to identify the attribute mastery patterns of the examinee in cognitive diagnosis assessment. There are many methods to classify the attribute mastery patterns and many studies have been done to diagnose what the individuals have mastered and or Montel Carl Computer Simulation is used to study the classification of the attribute mastery patterns by Deep Learning. Four results were found. Firstly, Deep Learning can be used to classify the attribute mastery patterns efficiently. Secondly, the complication of the structures will decrease the accuracy of the classification. The order of the influence is linear, convergent, unstructured and divergent. It means that the divergent is the most complicated, and the accuracy of this structure is the lowest among the four structures. Thirdly, with the increasing rates of the slipping and guessing, the accuracy of the classification decreased in verse, which is the same as the existing research results. At last, the results are influenced by the sample size of the training, and the proper sample size is in need of deeper discussion.

An Intelligent Medical Expert System Using Temporal Fuzzy Rules and Neural Classifier

As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.

肝豆状核变性新型生物标志物相对可交换铜及认知功能与中医证型的相关性研究

目的探讨肝豆状核变性(Wilson’s disease,WD)患者不同中医分型与认知功能及新型血清生物学指标相对可交换铜(relative exchangeable copper,REC)的相关性。方法将57例WD患者辨证分型为湿热内蕴证、痰瘀互结证及肝肾阴虚证,行蒙特利尔认知评估量表(MoCA)及阿尔茨海默病评估量表认知量表(Alzheimer’s disease assessment scale-cognitive subscale,ADAS-cog)评价认知功能,火焰原子吸收光谱法检测计算血清REC水平,评估不同证型WD患者认知功能及REC水平,同时分析认知及REC与中医证型之间的相关性,及其预测中医证型的诊断能力。结果湿热内蕴证组REC及ADAS-cog评分显著高于痰瘀互结证和肝肾阴虚证(湿热内蕴证vs痰瘀互结证,REC:P=0.016,ADAS-cog:P=0.035;湿热内蕴证vs肝肾阴虚证,REC:P<0.001,ADAS-cog:P=0.001);湿热内蕴证组MoCA评分显著低于痰瘀互结证和肝肾阴虚证(湿热内蕴证vs痰瘀互结证,P=0.047;湿热内蕴证vs肝肾阴虚证,P=0.004);痰瘀互结证与肝肾阴虚证相比,REC、MoCA及ADAS-cog评分差异均无统计学意义(均P>0.05)。Pearson相关分析显示,3组患者REC与MoCA均呈负相关,与ADAS-cog均呈正相关;ROC曲线分析显示,REC在评估湿热内蕴证的诊断价值最高。结论REC除了在WD诊断中的实用性外,还可用于评估WD认知损害(严重程度),REC及认知功能评价在评估WD主要中医证型具有一定的诊断参考价值。

基于机器学习与眼动追踪的认知风格模型构建

认知风格反映了学生接近、获取、组织、处理和解释信息的模式,可用来解释和指导学生的行为。将认知风格集成到智能系统中,有助于开发个性化的用户模型,推动智能教育发展。当前有关认知风格自动分类的研究较为匮乏,尚未将机器学习与眼动追踪技术联合起来进行应用。基于机器学习与眼动追踪的认知风格模型,选取注视时长、注视点数量、扫视时长、眼跳次数、眼跳距离与瞳孔直径等6个与认知有着密切关系的眼动指标,归一化处理后借助机器学习算法进行认知风格自动分类。实验结果表明:在进行同样时长的视频学习时,不同场认知风格的学习者表现出不同的视觉行为模式;场依存型学习者注视点较为分散,表现出有较多的扫视行为、较少的注视行为、较长的眼跳距离与较大的瞳孔直径变化,信息搜索效率较低;而场独立型学习者有着较为密集与定向的视觉注意模式,信息搜索效率更高。对5种机器学习算法进行性能对比后发现,逻辑回归算法的分类效果最好,准确率达到89.01%,Kappa值达到0.774。该认知风格自动化分类模型既可用于智能学习系统的课程资源优化设计,也可用于个性化学习路径的推荐。未来可整合更多生理数据,通过不同模态数据之间的信息互补,提升数据分析的准确性以及对学习者认知能力评估的可靠性。

基于教学活动主体认知的通用思政元素挖掘方法

为解决专业课教师在课程思政实施过程中思政元素挖掘能力不足的问题,采用逆向思维,在思政元素挖掘过程中以学生为中心,从学生角度出发,在考虑学生认知过程、学习经历、生活环境的前提下,选择思政素材,利用网络收集思政元素短视频,开展思政元素问卷信息统计和定量计算,实现通用思政元素的挖掘。结果表明,采用逆向思维开展教学主体认知反馈的通用思政元素挖掘方法,对教师开展课程思政教学有一定指导作用。

血管性认知障碍诊治进展

血管性认知障碍(VCI)是近年来在认知障碍问题、痴呆问题和脑血管问题的一个核心交汇点,已严重威胁着中老年人群的身体状况和生活品质,目前对于VCI的完整描述及统一诊断标准仍不够完善。现就VCI研究过程中不断更新的概念、分级、致病因素、诊疗、探测仪器和治疗处理方法进行综述,旨在为我国VCI的诊治提供参考。

基于“布鲁姆认知—成果导向”的“机车制动系统”课程教学模式研究

文章针对“机车制动系统”课程中存在的教学模式单一,教学资源缺乏系统性,教学评价不全面等问题,进一步将先进的教育理念深度地融入混合型教学中,提出一种基于布鲁姆认知目标理论和成果导向教育理念的“机车制动系统”课程教学模式研究,研究结果表明对该课程进行改革与实践研究具有非常大的意义,可以为其他专业核心课程的建设与改革做一次有益的探索和尝试。

对化学教学中“模型认知”的认识与思考

模型认知是高中化学课程标准的核心素养之一。当前在中学化学学科领域内,关于模型及建模的研究主要集中在模型认知视角下的教学研究、建模教学在中学化学中的应用、中学化学复习课中模型的建构与应用研究等三个方面。模型的定义和化学模型的分类能帮助教师和学生更好地认识模型。模型定义的核心表达是为了某种特定的目的,利用简化、抽象、类比等方法,对事物的本质进行表征,将抽象思维外显化。化学认知模型和化学认识模型共同指导化学学科知识的学习,基于模型的教学策略是落实模型认知素养的重要途径之一。

融合全局和局部特征的建筑物形状智能分类方法

深度学习方法支持下的建筑物形状认知成为地图制图等领域研究的热点,利用深度学习的特征挖掘能力,可以提取形状的嵌入表示,支撑制图综合、空间查询等应用场景。本文以建筑物数据为例,构建了一种融合全局特征和图节点特征的建筑物形状分类的图谱卷积神经网络模型。首先,在建筑物加权图基础上分别以建筑物4个宏观形状特征、边界顶点的多阶局部和区域结构特征生成形状的融合描述;然后,利用图谱卷积神经网络提取多层次形状信息,通过融合不同层的图表示结果生成特征编码用于形状分类。试验结果表明,相较对比方法,本文方法能够更有效地区分不同建筑物的形状类别,且生成的特征编码具有良好的形状区分度。

纵向非参数认知诊断评估

通过建立相邻时间点间被试知识状态和理想作答模式的链接,提出了4种逻辑简洁的纵向非参数认知诊断方法:LNPC、LWNPC、LGNPC和LWGNPC。模拟研究结果表明:建立的链接能提升纵向判准精度。与参数模型相比,4种方法估计精度相当,受样本容量影响小。与Long-HDD相比,4种方法判准精度较高,题目质量较低时LNPC和LWNPC仍有较好表现;实证研究表明:4种方法能够应用于实际纵向测验分析,与参数模型和Long-HDD判别一致性较高。推荐LWNPC方法。

非参数CD-CAT题库Q矩阵优化设计

开展非参数CD-CAT题库Q矩阵的优化设计,并将它用于指导题库题目编制或选择,且它无需模型参数估计,这对于促进非参数CD-CAT进入课堂评价至关重要.受p-优化的启发,该文提出比例法生成优化Q矩阵,同时将已有的参数化模型题库设计方法推广到非参数化题库Q矩阵的优化设计.模拟研究结果显示:比例法和推广的融合法都有较好表现,而且当题库量较少时比例法更有优势.