Text categorization based on combination of modified back propagation neural network and latent semantic analysis

This paper proposed a new text categorization model based on the combination of modified back propagation neural network (MBPNN) and latent semantic analysis (LSA). The traditional back propagation neural network (BPNN) has slow training speed and is easy to trap into a local minimum, and it will lead to a poor performance and efficiency. In this paper, we propose the MBPNN to accelerate the training speed of BPNN and improve the categorization accuracy. LSA can overcome the problems caused by using statistically derived conceptual indices instead of individual words. It constructs a conceptual vector space in which each term or document is represented as a vector in the space. It not only greatly reduces the dimension but also discovers the important associative relationship between terms. We test our categorization model on 20-newsgroup corpus and reuter-21578 corpus, experimental results show that the MBPNN is much faster than the traditional BPNN. It also enhances the performance of the traditional BPNN. And the application of LSA for our system can lead to dramatic dimensionality reduction while achieving good classification results.     

Using GMDH-based networks for improved spam detection and email feature analysis

Unsolicited or spam email has recently become a major threat that can negatively impact the usability of electronic mail. Spam substantially wastes time and money for business users and network administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium for distributing harmful code and/or offensive content. In this paper, we explore the application of the GMDH (Group Method of Data Handling) based inductive learning approach in detecting spam messages by automatically identifying content features that effectively distinguish spam from legitimate emails. We study the performance for various network model complexities using spambase, a publicly available benchmark dataset. Results reveal that classification accuracies of 91.7% can be achieved using only 10 out of the available 57 attributes, selected through abductive learning as the most effective feature subset (i.e. 82.5% data reduction). We also show how to improve classification performance using abductive network ensembles (committees) trained on different subsets of the training data. Comparison with other techniques such as neural networks and naïve Bayesian classifiers shows that the GMDH-based learning approach can provide better spam detection accuracy with false-positive rates as low as 4.3% and yet requires shorter training time.     

An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors

Internet of Things (IoT) is gradually adopted by many organizations to facilitate the information collection and sharing. In an organization, an IoT node usually can receive and send an email for event notification and reminder. However, unwanted and malicious emails are a big security challenge to IoT systems. For example, attackers may intrude a network by sending emails with phishing links. To mitigate this issue, email classification is an important solution with the aim of distinguishing legitimate and spam emails. Artificial intelligence especially machine learning is a major tool for helping detect malicious emails, but the performance might be fluctuant according to specific datasets. The previous research figured out that supervised learning could be acceptable in practice, and that practical evaluation and users' feedback are important. Motivated by these observations, we conduct an empirical study to validate the performance of common learning algorithms under three different environments for email classification. With over 900 users, our study results validate prior observations and indicate that LibSVM and SMO-SVM can achieve better performance than other selected algorithms.     

A three-layer back-propagation neural network for spam detection using artificial immune concentration

In this paper, a three-layer back-propagation neural network (BPNN) is employed for spam detection by using a concentration based feature construction (CFC) approach. In the CFC approach, ‘self’ and ‘non-self’ concentrations are constructed through ‘self’ and ‘non-self’ gene libraries, respectively, to form a two-element concentration vector for expressing the e-mail efficiently. A three-layer BPNN with two-element input is then employed to classify e-mails automatically. Comprehensive experiments are conducted on two public benchmark corpora PU1 and Ling to demonstrate that the proposed CFC approach based BPNN classifier not only has a very much fast speed but also achieves 97 and 99% of classification accuracy on corpora PU1 and Ling by just using a two-element concentration feature vector.     

A modified Smith predictive scheme based back-propagation neural network approach for FOPDT processes control

In this paper, a novel control scheme to deal with process uncertainties in the form of disturbance loads and modelling errors, as well as time-varying process parameters is proposed by applying the back-propagation neural network (BPNN) approach. A BPNN predictive controller that replaces the entire Smith predictor structure is initially trained offline. Lyapunov direct method is used to prove that the convergence of this BPNN is guaranteed by selecting a suitable learning rate during the learning process. However, the Smith predictor based BPNN control is an off-line training based algorithm, which is a time consuming method and requires a known process plant input from the controller. A desired control input to the process is difficult to obtain for the training of the network. As a result a group of proper training data (target control inputs and outputs) can hardly be provided. In order to overcome this problem, a BPNN with an on-line training algorithm is introduced for the control of a First Order plus Dead Time (FOPDT) process. The stability analysis is carried out using the Lyapunov criterion to demonstrate the network convergence ability. Simulation results show that this proposed online trained neural Smith predictor based controller provides excellent robustness to process modelling errors and disturbance loads, and high adaptability to time varying processes parameters.     

Spatio-temporal data classification using CVNNs

This paper presents two new approaches of spatio-temporal data classification using complex-valued neural networks. First approach uses extended complex-valued back-propagation algorithm to train MLP network, whose output’s amplitudes are encoded in one-of-N coding. It makes a classification decision based on accumulated distance between network output and trained pattern. The second approach is inspired in RBF networks with two layer architecture. Neurons from the first layer have fixed position in space and time encoded into theirs weights. This layer is trained by presented extension of neural gas algorithm into complex numbers. The second layer affects which neurons from the first layer belong to specific class. Paper contains details on experimenting with proposed approaches on artificial data of hand-written character recognition and comparison of both methods.     

An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.     

基于TSNE和多尺度稀疏自编码的高光谱图像分类

针对高光谱图像存在维数“灾难”、特征以及空间信息利用不足的问题,结合深度学习、流形学习及多尺度空间特征的最新进展,提出了一种TSNE和多尺度稀疏自编码网络的高光谱图像分类算法。利用TSNE算法对高光谱图像进行降维,再对每个像元的邻域进行多尺度空间特征提取,利用加入空谱联合信息的像元训练稀疏自编码网络模型并通过softmax分类器进行分类,减少计算复杂度,提高分类精确度。通过对Indian Pines及Pavia University两组数据进行实验,结果表明,提出的算法与其他五种算法相比分类效果更好。     

A comparative study for content-based dynamic spam classification using four machine learning algorithms

The growth of email users has resulted in the dramatic increasing of the spam emails during the past few years. In this paper, four machine learning algorithms, which are Naïve Bayesian (NB), neural network (NN), support vector machine (SVM) and relevance vector machine (RVM), are proposed for spam classification. An empirical evaluation for them on the benchmark spam filtering corpora is presented. The experiments are performed based on different training set size and extracted feature size. Experimental results show that NN classifier is unsuitable for using alone as a spam rejection tool. Generally, the performances of SVM and RVM classifiers are obviously superior to NB classifier. Compared with SVM, RVM is shown to provide the similar classification result with less relevance vectors and much faster testing time. Despite the slower learning procedure, RVM is more suitable than SVM for spam classification in terms of the applications that require low complexity.     

Integration of independent component analysis and neural networks for ECG beat classification

In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG.     

Automatic thesaurus construction for spam filtering using revised back propagation neural network

Email has become one of the fastest and most economical forms of communication. Email is also one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. This paper proposes a new spam filtering system using revised back propagation (RBP) neural network and automatic thesaurus construction. The conventional back propagation (BP) neural network has slow learning speed and is prone to trap into a local minimum, so it will lead to poor performance and efficiency. The authors present in this paper the RBP neural network to overcome the limitations of the conventional BP neural network. A well constructed thesaurus has been recognized as a valuable tool in the effective operation of text classification, it can also overcome the problems in keyword-based spam filters which ignore the relationship between words. The authors conduct the experiments on Ling-Spam corpus. Experimental results show that the proposed spam filtering system is able to achieve higher performance, especially for the combination of RBP neural network and automatic thesaurus construction.     

Static hand gesture recognition using neural networks

This paper presents a novel technique for hand gesture recognition through human–computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.     

基于神经网络集成的垃圾邮件过滤系统设计

垃圾邮件的处理是电子邮件服务中非常重要的功能,该文在对标准邮件集表示为向量空间模型,降维处理处理工作的基础上,运用神经网络集成的方法来构造邮件分类器,对邮件进行过滤;该方法在垃圾邮件语料库上进行了实验,实验证明该方法对于垃圾邮件的过滤有较好的效果。     

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

基于图像信息度量与关键词的邮件智能分类系统

如何利用邮件的正文与附件信息有效地实现其分类，是现在邮件处理领域一个重要的课题。该文从商业应用角度提出了一种基于图像信息度量与关键词的邮件智能过滤与分发方法，通过基于朴素贝叶斯分类器的邮件关键词信息处理，及附件图像信息的基于归一化PIM文本图像检测理论的分析，能够综合运用邮件正文、地址等文本信息与附件图像信息作为分类的评价参数，有效地实现了邮件的智能分类。     

基于深度学习的无线传感器网络数据融合

在无线传感器网络数据融合算法中,BP神经网络被广泛用于节点数据的特征提取和分类。为了解决BP神经网络收敛慢,易陷入局部最优值且泛化能力差从而影响数据融合效果的问题,提出一种将深度学习技术和分簇协议相结合的数据融合算法SAESMDA。SAESMDA用基于层叠自动编码器(SAE)的深度学习模型SAESM取代BP神经网络,算法首先在汇聚节点训练SAESM并对网络分簇,接着各簇节点通过SAESM对采集数据进行特征提取,之后由簇首将分类融合后的特征发送至汇聚节点。仿真实验表明,和采用BP神经网络的BPNDA算法相比,SAESMDA在网络能耗大致相同的情况下具有更高的特征提取分类正确率。     

Social feature-based enterprise email classification without examining email contents

Without imposing restrictions, many enterprises find nonwork-related contents consuming network resources. Business communication over emails thus incurs undesired delays and inflicts damages to businesses, explaining why many enterprises are concerned with the competition to use email services. Obviously, enterprises should prioritize business emails over personal ones in their email service. Therefore, previous works present content-based classification methods to categorize enterprise emails into business or personal correspondence. Accuracy of these methods is largely determined by their ability to survey as much information as possible. However, in addition to decreasing the performance of these methods, monitoring the details of email contents may violate privacy rights that are under legal protection, requiring a careful balance of accurately classifying enterprise emails and protecting privacy rights. The proposed email classification method is thus based on social features rather than a survey of emails contents. Social-based metrics are also designed to characterize emails as social features; the obtained features are treated as an input of machine learning-based classifiers for email classification. Experimental results demonstrate the high accuracy of the proposed method in classifying emails. In contrast with other content-based methods that examine email contents, the emphasis on social features in the proposed method is a promising alternative for solving similar email classification problems.     

QPSO算法优化BP网络的网络流量预测

网络流量预测对于大规模网络的规划设计和网络资源管理等方面都具有积极的意义,是网络流量工程重要组成部分。结合QPSO算法和BP神经网络的优势,采用QPSO算法对BP神经网络的权值和阈值进行优化,并利用历史记录训练BP网络。仿真实验表明,与PSO训练的BP网络以及直接用BP网络进行预测的模型相比,基于QPSO训练的BP网络流量预测模型具有更好的预测能力。     

基于一维卷积神经网络的网络流量分类方法

针对传统机器学习算法对于流量分类的瓶颈问题,提出基于一维卷积神经网络模型的应用程序流量分类算法。将网络流量数据集进行数据预处理,去除无关数据字段,并使数据满足卷积神经网络的输入特性。设计了一种新的一维卷积神经网络模型,从网络结构、超参数空间以及参数优化方面入手构造了最优分类模型。该模型通过卷积层自主学习数据特征,解决了传统基于机器学习的流量分类算法中特征选择问题。通过网络公开数据集进行模型测试,相比于传统的一维卷积神经网络模型,所设计的神经网络模型的分类准确率提升了16.4%,总分类时间节省了71.48%。另外在类精度、召回率以及[F1]分数方面都有较好的提升。