Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System

Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis. The proposed MKL with ANFIS based deep learning method follows two-fold approach. MKL method is used to divide parameters between heart disease patients and normal individuals. The result obtained from the MKL method is given to the ANFIS classifier to classify the heart disease and healthy patients. Sensitivity, Specificity and Mean Square Error (MSE) are calculated to evaluate the proposed MKL with ANFIS method. The proposed MKL with ANFIS is also compared with various existing deep learning methods such as Least Square with Support Vector Machine (LS with SVM), General Discriminant Analysis and Least Square Support Vector Machine (GDA with LS-SVM), Principal Component Analysis with Adaptive Neuro-Fuzzy Inference System (PCA with ANFIS) and Latent Dirichlet Allocation with Adaptive Neuro-Fuzzy Inference System (LDA with ANFIS). The results from the proposed MKL with ANFIS method has produced high sensitivity (98%), high specificity (99%) and less Mean Square Error (0.01) for the for the KEGG Metabolic Reaction Network dataset.     

Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System

In this paper, an automatic diagnosis system based on Linear Discriminant Analysis (LDA) and Adaptive Network based on Fuzzy Inference System (ANFIS) for hepatitis diseases is introduced. This automatic diagnosis system deals with the combination of feature extraction and classification. This automatic hepatitis diagnosis system has two stages, which feature extraction – reduction and classification stages. In the feature extraction – reduction stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Then, the number of these features was reduced to 8 from 19 by using Linear Discriminant Analysis (LDA). In the classification stage, these reduced features are given to inputs ANFIS classifier. The correct diagnosis performance of the LDA-ANFIS automatic diagnosis system for hepatitis disease is estimated by using classification accuracy, sensitivity and specificity analysis, respectively. The classification accuracy of this LDA-ANFIS automatic diagnosis system for the diagnosis of hepatitis disease was obtained in about 94.16%.     

自适应混沌粒子群算法对极限学习机参数的优化

针对极限学习机（ELM）在处理非线性数据时效果不理想,并且ELM的参数随机化不利于模型泛化的特点,提出了一种改进的极限学习机算法。结合自适应混沌粒子群（ACPSO）算法对ELM的参数进行优化,以增强算法的稳定性,提高ELM对基因表达数据分类的精度。在UCI基因数据集上进行仿真实验,实验结果表明,与探测粒子群-极限学习机（DPSO-ELM）、粒子群-极限学习机（PSO-ELM）等算法相比,自适应混沌粒子群-极限学习机（ACPSO-ELM）算法具有较好的稳定性、可靠性,且能有效提高基因分类精度。     

An efficient 3D face recognition approach using local geometrical signatures

This paper presents a computationally efficient 3D face recognition system based on a novel facial signature called Angular Radial Signature (ARS) which is extracted from the semi-rigid region of the face. Kernel Principal Component Analysis (KPCA) is then used to extract the mid-level features from the extracted ARSs to improve the discriminative power. The mid-level features are then concatenated into a single feature vector and fed into a Support Vector Machine (SVM) to perform face recognition. The proposed approach addresses the expression variation problem by using facial scans with various expressions of different individuals for training. We conducted a number of experiments on the Face Recognition Grand Challenge (FRGC v2.0) and the 3D track of Shape Retrieval Contest (SHREC 2008) datasets, and a superior recognition performance has been achieved. Our experimental results show that the proposed system achieves very high Verification Rates (VRs) of 97.8% and 88.5% at a 0.1% False Acceptance Rate (FAR) for the “neutral vs. nonneutral” experiments on the FRGC v2.0 and the SHREC 2008 datasets respectively, and 96.7% for the ROC III experiment of the FRGC v2.0 dataset. Our experiments also demonstrate the computational efficiency of the proposed approach.     

Partially-supervised learning from facial trajectories for face recognition in video surveillance

Face recognition (FR) is employed in several video surveillance applications to determine if facial regions captured over a network of cameras correspond to a target individuals. To enroll target individuals, it is often costly or unfeasible to capture enough high quality reference facial samples a priori to design representative facial models. Furthermore, changes in capture conditions and physiology contribute to a growing divergence between these models and faces captured during operations. Adaptive biometrics seek to maintain a high level of performance by updating facial models over time using operational data. Adaptive multiple classifier systems (MCSs) have been successfully applied to video-to-video FR, where the face of each target individual is modeled using an ensemble of 2-class classifiers (trained using target vs. non-target samples). In this paper, a new adaptive MCS is proposed for partially-supervised learning of facial models over time based on facial trajectories. During operations, information from a face tracker and individual-specific ensembles is integrated for robust spatio-temporal recognition and for self-update of facial models. The tracker defines a facial trajectory for each individual that appears in a video, which leads to the recognition of a target individual if the positive predictions accumulated along a trajectory surpass a detection threshold for an ensemble. When the number of positive ensemble predictions surpasses a higher update threshold, then all target face samples from the trajectory are combined with non-target samples (selected from the cohort and universal models) to update the corresponding facial model. A learn-and-combine strategy is employed to avoid knowledge corruption during self-update of ensembles. In addition, a memory management strategy based on Kullback–Leibler divergence is proposed to rank and select the most relevant target and non-target reference samples to be stored in memory as the ensembles evolves. For proof-of-concept, a particular realization of the proposed system was validated with videos from Face in Action dataset. Initially, trajectories captured from enrollment videos are used for supervised learning of ensembles, and then videos from various operational sessions are presented to the system for FR and self-update with high-confidence trajectories. At a transaction level, the proposed approach outperforms baseline systems that do not adapt to new trajectories, and provides comparable performance to ideal systems that adapt to all relevant target trajectories, through supervised learning. Subject-level analysis reveals the existence of individuals for which self-updating ensembles with unlabeled facial trajectories provides a considerable benefit. Trajectory-level analysis indicates that the proposed system allows for robust spatio-temporal video-to-video FR, and may therefore enhance security and situation analysis in video surveillance.     

基于智能全间隔自适应模糊支持向量机的水质分类

提出了一种新型具有良好特性的支持向量机--全间隔自适应模糊支持向量机(TAFSVM)。运用实值遗传算法(RGA)对其进行参数优选,得到一种新的智能模型--实值遗传算法优化的全间隔自适应模糊支持向量机(RGATAFSVM)模型,并且应用于四种不同的水质数据分类。实验结果表明,提出的模型相对标准支持向量机、BP神经网络和单因子分类方法具有较高的分类精度和较高的稳定性,是一种有效的水质分类方法。     

基于支持向量机的人脸识别方法

提出一种基于二值边缘图像和支持向量机的人脸识别方法,以具有较强光照鲁棒性的二值边缘图像作为人脸表征,用支持向量机来分类。其中二值边缘图像是用一种基于Sobel算子的局部自适应阂值选取边缘检测算法。仿真实验结果表明对于有165幅人脸的Yale人脸库识别率可达92．73％,而对于有798幅人脸图像的AR人脸库识别率可达95．62％,而且该方法对有光照变化的人脸图像有较好的鲁棒性。     

基于深度神经网络的特征加权融合人脸识别方法

针对目前难以提取到适合用于分类的人脸特征以及在非限条件下进行人脸识别准确率低的问题,提出了一种基于深度神经网络的特征加权融合人脸识别方法(DLWF)。首先,应用主动形状模型(ASM)提取出人脸面部的主要特征点,并根据主要特征点对人脸不同器官区域进行采样;然后,将所得采样块分别输入到对应的深度信念网络(DBN)中进行训练,获得网络最优参数;最后,利用Softmax回归求出各个区域的相似度向量,将多区域的相似度向量加权融合得到综合相似度评分进行人脸识别。经ORL和WFL人脸库上进行实验验证,DLWF算法的识别准确率分别达到97%和88.76%,与传统算法主成分分析(PCA)、支持向量机(SVM)、DBN及FIP+线性判别式分析(LDA)相比,无论是限制条件还是非限制条件下,识别率均有提高。实验结果表明,该算法具有高效的人脸识别能力。     

Development of Algorithm for Person Re-Identification Using Extended Openface Method

Deep learning has risen in popularity as a face recognition technology in recent years. Facenet, a deep convolutional neural network (DCNN) developed by Google, recognizes faces with 128 bytes per face. It also claims to have achieved 99.96% on the reputed Labelled Faces in the Wild (LFW) dataset. However, the accuracy and validation rate of Facenet drops down eventually, there is a gradual decrease in the resolution of the images. This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images. The proposed system Extended Openface performs facial recognition by using three different features i) facial landmark ii) head pose iii) eye gaze. It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model (SGEN-CLM). It also detects the head pose and eye gaze using Enhanced Constrained Local Neural field (ECLNF). Extended openface employs a simple Support Vector Machine (SVM) for training and testing the face images. The system’s performance is assessed on low-resolution datasets like LFW, Indian Movie Face Database (IMFDB). The results demonstrated that Extended Openface has a better accuracy rate (12%) and validation rate (22%) than Facenet on low-resolution images.     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

基于局部二元模式的面部表情识别研究

提出了一种基于局部二元模式（Local Binary Pattern,LBP）与支持向量机（SVM）相结合的面部表情识别方法。使用LBP算子对图像进行处理,对图像的模式进行统计形成面部表情特征;使用线性判别分析对表情特征进行降维处理;采用支持向量机对面部表情进行分类。用Matlab实现了上述方法,并在日本女性人脸表情（JAFFE）数据库上测试,取得了70.95%的识别率。     

基于深度对齐网络的生成对抗网络伪造人脸检测

针对现有的生成对抗网络（GAN）伪造人脸图像检测方法在有角度及遮挡情况下存在的真实人脸误判问题,提出了一种基于深度对齐网络（DAN）的GAN伪造人脸图像检测方法。首先,基于DAN设计面部关键点提取网络,以提取真伪人脸关键点位置;然后,采用主成分分析（PCA）方法将每一组关键点映射到三维空间,从而减少冗余信息以及降低特征维度;最后,利用支持向量机（SVM）五折交叉验证对特征进行分类,并计算准确率。实验结果表明,该方法通过提高面部关键点定位准确度改善了由于定位误差引起的面部不协调问题,进而降低了真实人脸误判率。与VGG19、XceptionNet和Dlib-SVM方法相比,正脸情况下,该方法的ROC下面积（AUC）值提高了4.48到32.96个百分点,平均精度（AP）提高了4.26到33.12个百分点;有角度及遮挡人脸情况下,该方法的AUC值提高了10.56到30.75个百分点,AP提高了7.42到42.45个百分点。     

Selection of Relevant Visual Feature Sets for Enhanced Depression Detection using Incremental Linear Discriminant Analysis

Presently, while automated depression diagnosis has made great progress, most of the recent works have focused on combining multiple modalities rather than strengthening a single one. In this research work, we present a unimodal framework for depression detection based on facial expressions and facial motion analysis. We investigate a wide set of visual features extracted from different facial regions. Due to high dimensionality of the obtained feature sets, identification of informative and discriminative features is a challenge. This paper suggests a hybrid dimensionality reduction approach which leverages the advantages of the filter and wrapper methods. First, we use a univariate filter method, Fisher Discriminant Ratio, to initially reduce the size of each feature set. Subsequently, we propose an Incremental Linear Discriminant Analysis (ILDA) approach to find an optimal combination of complementary and relevant feature sets. We compare the performance of the proposed ILDA with the batch-mode LDA and also the Composite Kernel based Support Vector Machine (CKSVM) method. The experiments conducted on the Distress Analysis Interview Corpus Wizard-of-Oz (DAIC-WOZ) dataset demonstrate that the best depression classification performance is obtained by using different feature extraction methods in combination rather than individually. ILDA generates better depression classification results in comparison to the CKSVM. Moreover, ILDA based wrapper feature selection incurs lower computational cost in comparison to the CKSVM and the batch-mode LDA methods. The proposed framework significantly improves the depression classification performance, with an F1 Score of 0.805, which is better than all the video based depression detection models suggested in literature, for the DAIC-WOZ dataset. Salient facial regions and well performing visual feature extraction methods are also identified.

     

Modeling Beliefs and Solution Strategies in a Distributed Learning System

We present algorithms guiding the identification of student solution strategies in the Domain-Independent Adaptive Tutoring System (DIATS). The DIATS is a distributed computer-assisted instruction system with a prototype in the domain of psychiatric mental disorders. Problems are solved using differential diagnosis decision trees from the DMS-IV-TR. Student solution strategy identification is performed by the Response Analysis Unit of the modeler. The Domain-Independent Adaptive Tutoring System (DIATS) has a distributed architecture that combines shared-data and client-server styles.     

Texture and shape information fusion for facial expression and facial action unit recognition

A novel method based on fusion of texture and shape information is proposed for facial expression and Facial Action Unit (FAU) recognition from video sequences. Regarding facial expression recognition, a subspace method based on Discriminant Non-negative Matrix Factorization (DNMF) is applied to the images, thus extracting the texture information. In order to extract the shape information, the system firstly extracts the deformed Candide facial grid that corresponds to the facial expression depicted in the video sequence. A Support Vector Machine (SVM) system designed on an Euclidean space, defined over a novel metric between grids, is used for the classification of the shape information. Regarding FAU recognition, the texture extraction method (DNMF) is applied on the differences images of the video sequence, calculated taking under consideration the neutral and the expressive frame. An SVM system is used for FAU classification from the shape information. This time, the shape information consists of the grid node coordinate displacements between the neutral and the expressed facial expression frame. The fusion of texture and shape information is performed using various approaches, among which are SVMs and Median Radial Basis Functions (MRBFs), in order to detect the facial expression and the set of present FAUs. The accuracy achieved using the Cohn–Kanade database is 92.3% when recognizing the seven basic facial expressions (anger, disgust, fear, happiness, sadness, surprise and neutral), and 92.1% when recognizing the 17 FAUs that are responsible for facial expression development.     

基于NSCT和M-PCNN的人脸特征提取

为提高人脸识别对人脸姿态、位置、表情变化的鲁棒性,提出一种基于非下采样Contourlet变换(NSCT)与改进脉冲耦合神经网络(M-PCNN)的人脸特征提取方法。利用NSCT对输入图像进行多尺度分解和多方向稀疏分解,以捕获图像中的高维奇异信息,使用M-PCNN模型提取各子带的信息熵,将其作为人脸特征,利用支持向量机(SVM)实现分类与识别。仿真结果表明,该方法鲁棒性较强,在识别和分类中表现出较好的性能。     

Hybrid of simulated annealing and SVM for hydraulic valve characteristics prediction

Accurate prediction for the synthesis characteristics of hydraulic valve in industrial production plays an important role in decreasing the repair rate and the reject rate of the product. Recently, Support Vector Machine (SVM) as a highly effective mean of system modeling has been widely used for predicting. However, the important problem is how to choose the reasonable input parameters for SVM. In this paper, a hybrid prediction method (SA–SVM for short) is proposed by using simulated annealing (SA) and SVM to predict synthesis characteristics of the hydraulic valve, where SA is used to optimize the input parameters of SVM based prediction model. To validate the proposed prediction method, a specific hydraulic valve production is selected as a case study. The prediction results show that the proposed prediction method is applicable to forecast the synthesis characteristics of hydraulic valve and with higher accuracy. Comparing with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are also made.     

基于自适应LBP和SVM的织物疵点检测算法

为准确提取不同种类织物纹理的特征,提出一种新的纹理特征描述方法——自适应局部二值模式(ALBP)。该方法为不同纹理结构创建相应的主要概率模式子集,避免了均匀局部二值模式(ULBP)使用同一模式集描述不同纹理而导致的描述不准确问题。在该算法基础上构建一种基于支持向量机(SVM)的织物疵点检测算法,将疵点检测问题转化为分类问题。实验结果证明,该算法不仅保持了传统局部二值模式(LBP)的旋转不变、多分辨率等特点,而且疵点检测结果在视觉上更加清晰、误检率更低、适用范围更广,SVM的优秀分类性能也有效地提高了疵点检测的准确率。     

自适应模糊支持向量机算法

支持向量机算法对噪声和异常点是敏感的,为了克服这个问题,人们引入了模糊隶属度。传统确定样本模糊隶属度的方法,都是基于原始空间的。文章提出了基于特征空间的模糊隶属度函数模型。在该模型中,以特征空间中的样本为中心,以给定的距离d为半径作超球,根据其它样本落到超球内的个数来确定中心样本点的模糊隶属度。并将新的模糊隶属度模型引入自适应支持向量机,提出了模糊自适应支持向量机算法。实验结果表明,该模型能有效地提高自适应支持向量机的抗噪能力和预测精度。     

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA–MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA–MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%.