Applications Classification of VPN Encryption Tunnel Based on SAE-2dCNN Model

How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique.Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel.Various VPN protocols make the feature engineering of machine learning extremely difficult.Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification.This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model.SAE can effectively reduce the dimensionality of the data,which not only improves the training efficiency of 2dCNN,but also extracts more precise features and improves accuracy.This paper uses the most common VPN encryption data in the real network to train and test the model.The test results verify the effectiveness of the SAE-2dCNN model.

Reliability and validity of sub-axial injury classification in clinical application

Objective To evaluate the clinical reliability and validity of the sub-axial injury classification (SLIC) system proposed by the Spine Trauma Study Group (STSG) in 2007. Methods Thirty cases of cervical injury were randomly chosen

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

Nutrition and Applications of Rice Bran Oil: a Mini-overview

Rice bran oil is a healthy oil from many aspects.The oil has a balanced fatty acid profile comparing with many other vegetable oils.The key difference is the minor components or micronutrients or unsaponifiable matters contained in the oil that are very special and in larger percentages.The oil contains more than 1.5%oryzanol that gives nutritional and pharmaceutical functions from the studies so far.More studies are needed to demonstrate the wide functions in many aspects.The oil also contains large percentage of phytosterols which received huge amount of studies for nutritional applications.Furthermore,the oil contains tocopherols and tocotrienols,in which for the later particularly it gives many special functions including prevention of breast cancers for example.When the oil is properly processed and used in foods,those functions are more and more demonstrated in nutritional or biological studies.Thus the oil in food and pharmaceutical applications is in exploring both in academic studies and industrial practice.In this work,an overview of such progress is given.

The ARMA model’s pole characteristics of Doppler signals fromthe carotid artery and their classification application

In order to diagnose the cerebral infarction, a classification system based on the ARMA model and BP (Back-Propagation) neural network is presented to analyze blood flow Doppler signals from the carotid artery. In this system, an ARMA model is first used to analyze the audio Doppler blood flow signals from the carotid artery. Then several characteristic parameters of the pole's distribution are estimated. After studies of these characteristic parameters' sensitivity to the textcolor cerebral infarction diagnosis, a BP neural network using sensitive parameters is established to classify the normal or abnormal state of the cerebral vessel. With 474 cases used to establish the appropriate neural network, and 52 cases used to test the network, the results show that the correct classification rate of both training and testing are over 94%. Thus this system is useful to diagnose the cerebral infarction.

Application of a multilayer feedforward network to voiced-unvoiced-silence classifications of speech

A procedure has been developed for making voiced, unvoiced, and silence classifications of speech by using a multilayer feedforward net -work. Speech signals were analyzed sequentially and a feature vector was obtained for each segment . The feature vector served as input to a 3-layer feedforward network in which voiced, unvoiced, and silence classification was made. The network had a 6-12-3 node architecture and was trained using the generalized delta rule for back propagation of error . The performance of the network was evaluated using speech samples from 3 male and 3 female speakers . A speaker-dependent classification rate of 94.7% and speaker-independent classification rate of 94.3% were obtained. It is concluded that the voiced, unvoiced , and silence classification of speech can be effectively accomplished using a multilayer feedforward network.

Software Analytics of Open Source Business Software

This paper applies software analytics to open source code. Open-source software gives both individuals and businesses the flexibility to work with different parts of available code to modify it or incorporate it into their own project. The open source software market is growing. Major companies such as AWS, Facebook, Google, IBM, Microsoft, Netflix, SAP, Cisco, Intel, and Tesla have joined the open source software community. In this study, a sample of 40 open source applications was selected. Traditional McCabe software metrics including cyclomatic and essential complexities were examined. An analytical comparison of this set of metrics and derived metrics for high risk software was utilized as a basis for addressing risk management in the adoption and integration decisions of open source software. From this comparison, refinements were added, and contemporary concepts of design and data metrics derived from cyclomatic complexity were integrated into a classification scheme for software quality. It was found that 84% of the sample open source applications were classified as moderate low risk or low risk indicating that open source software exhibits low risk characteristics. The 40 open source applications were the base data for the model resulting in a technique which is applicable to any open source code regardless of functionality, language, or size.