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先进纺织材料及加工技术国际学术会议

作者： p potluri p J Hogg Northwest Composites Centre University of Manchester

＜正＞1 Introduction The composites industry is facing a period of enormous change as it is faced with a new set of industrial priorities set by the major end users of composite structures. The most pressing and finan...

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An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

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Computer Systems Science & Engineering 2023年第4期45卷 149-166页

作者： p.Sherubha S.p.Sasirekha A.Dinesh Kumar Anguraj J.Vakula Rani Raju Anitha S.phani praveen R.Hariharan Krishnan Department of Information Technology Karpagam College of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatoreTamilnaduIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationAndhra PradeshIndia Department of MCA CMR Institute of TechnologyBengaluruKarnatakaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyAndhra PradeshIndia Department of Computer Science and Engineering Residency CollegeChennaiIndia

The Cloud system shows its growing functionalities in various industrial *** safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill ***,Machine Learning(ML)approaches have been used for the construction of intellectual *** IDS are based on ML techniques either as unsupervised or *** supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack ***,the unsupervised model fails to provide a satisfactory ***,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good ***,the Naïve Bayes classifier is used for classification *** approach exposes the finest generalization ability to train the *** unlabelled data is also used for adoption towards data ***,redundant and noisy samples over the dataset are *** validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD *** experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer perceptrons(MLp),Support Vector Machine(SVM),and ***,False Alarm Rate(FAR)and True positive Rate(TpR)of Naive Bayes(NB)is 0.3 and 0.99,*** compared to prevailing techniques,the anticipated approach also delivers promising outcomes.

关键词： Network intrusion detection system feature selection auto-encoder support vector machine(SVM) anomaly

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A novel convolutional neural network with gated recurrent unit for automated speech emotion recognition and classification

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Journal of Control and Decision 2023年第1期10卷 54-63页

作者： p.Ravi prakash D.Anuradha Javid Iqbal Mohammad Gouse Galety Ruby Singh S.Neelakandan Department of IT Prasad V Potluri Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science and Business Systems Panimalar Engineering CollegeChennaiIndia Institute of Computer Science and Digital Technology(ICSDI) UCSI UniversityKuala LumpurMalaysia Department of Information Technology College of IT&CSCatholic University in ErbilErbilIraq Department of CSE SRM Institute of Science and TechnologyGhaziabadUttar PradeshIndia Department of CSE R.M.K Engineering CollegeSriperumbudurIndia

Automated Speech Emotion Recognition (SER) becomes more popular and has increased *** concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly depends upon the in-depth analysis of the speech signal,extracts features containing emotional details from the speech signal, and utilises patternrecognition techniques for emotional state identification. The major problem in automatic SERis to extract discriminate, powerful, and emotional salient features from the acoustical content ofspeech signals. The proposed model aims to detect and classify three emotional states of speechsuch as happy, neutral, and sad. The presented model makes use of Convolution neural network– Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set offeature vectors. A comprehensive simulation takes place using the Berlin German Database andSJTU Chinese Database which comprises numerous audio files under a collection of differentemotion labels.

关键词： Emotion recognition speech recognition deep learning classification model Berlin emotion dataset

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