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Sequential pattern Analysis of Emotion in Speech With LSTM 2

Sequential Pattern Analysis of Emotion in Speech With LSTM

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2nd IEEE international conference on Device Intelligence, computing and Communication Technologies, DICCT 2024

作者： Upadhyay, Deepak Tiwari, Ananya Niathani, Himanshi Graphic Era Hill University Dept.of Cse Dehradun India

ISBN: (纸本)9798350372847

Automatic speech emotion recognition poses great challenges due to the large differences between acoustic properties and human perception. its performance relies on a variety of acoustic discriminations suited to specific tasks. it makes things difficult when people share different opinions. speech emotions have different strengths with different sounds at different points in the middle phase. therefore, speech recognition has become a difficult task in computer processing. this article introduces deep learning techniques and discusses some recent literature applying these techniques to speech recognition. in this context, the recurrent neural network (rnn) algorithm has become the basis of speech recognition. the classifers used to detect emotions like happiness, surprise, anger, neutral state, sadness, etc. the foundation of the speech emotion recognition system lies in a database comprising emotional speech samples. extracted from these samples are key features such as energy, pitch, and mel frequency cepstrum coefficient (mfcc). the evaluation of classification performance is contingent on the analysis of these extracted features. it includes different models for emotion recognition that use separate components to distinguish emotions. materials needed to train emotional speech may include speech patterns. the librosa package helps remove obtrusive features from the speech model. the key to classification is based on the quality of extracted features. finally, through this process we can identify and analyze the emotional content embedded in a speech signal. © 2024 IEEE.

关键词： Emotion recognition

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Proceedings - 2nd IEEE international conference on Device Intelligence, computing and Communication Technologies, DICCT 2024

Proceedings - 2nd IEEE International Conference on Device In...

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2nd IEEE international conference on Device Intelligence, computing and Communication Technologies, DICCT 2024

ISBN: (纸本)9798350372847

The proceedings contain 129 papers. The topics discussed include: investigation of retransmission timeout forecasting in multihop wireless links;design of RF sensor for human neck motion detection;an enhanced deep learning approach for disease classification from respiratory sound;sequential pattern analysis of emotion in speech with LSTM;Internet of Things IoMT data aggregation using machine learning;fake clones for adversaries detection with efficient relay selection in MWSN;early forest fire prediction system using wireless sensor network;a soft computing approach for efficient diagnosis of otitis media infection by mucosal disease early detection and referrals;abusive words detection on reddit comments using machine learning algorithms;and optimal placement and sizing of re generators to minimize loss and improve voltage profile in radial distribution network using PSO.

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Blockchain and Machine Learning for Advanced pattern recognition in Biometric Security Systems 7

Blockchain and Machine Learning for Advanced Pattern Recogni...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Mayuri, K. Smitha, B.S. Nijhawan, Ginni Rao, Bolleddu Devananda Gupta, Shubhi Albawi, Ali Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Amity University Amity School of Engineering Technology Uttar Pradesh Greater Noida India The Islamic University Radiology Techniques Department College of Medical Technology Najaf Iraq

ISBN: (纸本)9798350350067

Blockchain and machine learning allow biometric security systems to recognize complex patterns. This study analyzes how two unique technologies may improve fingerprint system safety and reliability. To demonstrate blockchain and machine learning in biometrics. The main reason for this study is that fingerprint protection is becoming more important in today's digital world. There needs to be strong name verification right away because there is more sensitive data and privacy issues. Traditional biometric systems have come a long way, but there are still problems with data security, privacy, and being able to grow. This study suggests important worries about how blockchain technology and machine learning might be able to improve pattern recognition, make security measures stricter, make data unchangeable, and reach the level of scalability needed for widespread use. Upon careful study of related approaches and works, biometric security could be improved by combining the decentralized and unchangeable record of blockchain with the pattern recognition abilities of machine learning. Ten important ways are looked at, and each one has its own pros and cons. A trio of CNNs, SVMs, and RNNs are used in the method we suggest. The suggested blockchain and machine learning fusion is shown to be better in a detailed analysis. The study ends with compared tables that show the parameters and performance evaluations of 10 similar systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Text Proofreading Method for Tibetan Sound Similarity Syllables Based on soft-Masked BERT 4

Text Proofreading Method for Tibetan Sound Similarity Syllab...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Zheng, Rengong Zhu, Jie Dunzhu, Ciren Provincial and Ministerial Collaborative Center for Tibetan Informatization Tibet University Lhasa China

ISBN: (纸本)9798350324303

Error detection and error correction are two very important elements in the field of text proofreading research. In this paper, we propose an algorithm to construct a Tibetan sound similarity syllables confusion set, which generates a large amount of tagged data by adding noise to the correct Tibetan text, and use the soft-masked technique to construct a model for Tibetan text proofreading, which consists of an error detection network and an error correction network. Experimental results on two different test sets show that the method can effectively detect and correct the wrong syllables in the text. © 2023 IEEE.

关键词： Error correction

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Comprehensive C2 Analysis and Anomaly Detection in HTTP Traffic: A MongoDB-Based Approach 3

Comprehensive C2 Analysis and Anomaly Detection in HTTP Traf...

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Pranav, H. Suryaa, E. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Bengaluru India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

In the evolving cybersecurity landscape, targeted attacks by sophisticated adversaries pose a significant challenge. These attackers aim to steal high-level employees' system privileges, enabling unauthorized access to crucial information. Traditional security measures often fall short against these advanced threats. Real Intelligence Threat Analytics (RITA) is a proactive solution that uses network traffic analysis to detect malicious activities. This project aims to enhance RITA by incorporating advanced techniques like Command and Control (C2) and Transport Layer Security (TLS) fingerprinting. These methods analyze communication patterns and cryptographic handshake parameters, respectively, to detect covert channels used by attackers and distinguish between legitimate and malicious encrypted communication. This approach strengthens cybersecurity defenses, ensuring organizations have a resilient mechanism to counter modern cyber threats and protect sensitive information.

关键词： Beacons Bowley's Skewness C2 fingerprinting Gaussian Mixture Models MongoDB pattern recognition Time Stamps RITA TLS fingerprinting Zeek Logs

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A Comparative Study of ConvNets based on Finger Vein Biometrics 7

A Comparative Study of ConvNets based on Finger Vein Biometr...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Chavan, Anuj Arora, Prerna Raghuwanshi, Ishika Kulkarni, Sujata Sardar Patel Institute of Technology Department of Electronics and Telecommunication Mumbai India

ISBN: (纸本)9798350397253

Gaining extraordinary attention in recent years, Finger Vein Biometrics has an ideal viability with the advantages of being the least susceptible to identity theft and being present inside the body, making it difficult to forge and be affected by any external factors. Although methods of extracting information from the veins are still under investigation, one of the critical stages of biometrics is recognition and matching to the individual under test. It is carried out with techniques of explicit feature extraction, which add to the extra computation and are hence deprecated and unnecessary. pattern-matching methods using feature extraction techniques add to the time and memory complexity of the models. Instead, one can use machine learning models, like CNNs and 'commercial ConvNets' (MobileNET, ResNET, DenseNET, etc.), to obtain an automated feature map for pattern matching with high accuracy. This article presents specially customized CNNs to achieve high matching accuracy for finger vein recognition and compares performance with commercial ConvNET models. © 2023 IEEE.

关键词： pattern matching

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Enhancing Sentiment Analysis Accuracy through Data driven Hyperparameter Optimization Using Convolutional Neural Network 3

Enhancing Sentiment Analysis Accuracy through Data driven Hy...

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Muppagowni, Ganesh Karthik Pujari, Vishnu Swarup Varampati, Tejaswini Muthyala, Ruchitha Chowdary Gutta Sambangi, Abhinav GITAM Univ GITAM Sch Technol Dept CSE Bengaluru Campus Bengaluru India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Every dataset has its unique kind of data and individual pieces of information. Certain configurations exhibit promising results across diverse datasets. This paper discusses the identification of optimal hyperparameters for sentiment analysis through systematic experimentation. In this implementation, Convolutional Neural Network (CNN) is used for sentiment analysis by interpreting facial expressions to identify seven emotions: happy, sad, neutral, angry, fearful, disgusted, and surprised. However, these methods can still get better because sometimes don't work perfectly due to various reasons like how quickly the algorithm learns and how much data it processes at once. Our objective is to enhance the accuracy of sentiment analysis models by identifying hyperparameter configurations that serve as effective starting points, acknowledging that these initial settings may not be perfect but can provide a solid foundation for subsequent refinement.

关键词： CNN Face Detection Haar Cascade Sentimental Analysis pattern recognition Hyperparameter Adjustment Open CV

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Adaptive Non-local Means Filter Based on Multi-kernel for Complicated Noise 1

引用

20th international conference on Intelligent computing (ICIC)

作者： Long, Qian Qu, Hongwei Wang, Yiping Wang, Gaihua Zhu, Bolun Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Elect Informat Inst Wuhan 430019 Peoples R China

ISBN: (数字)9789819756001

ISBN: (纸本)9789819755998;9789819756001

In the paper, we propose a modified denoising filter based on multi-kernel for color images. To compare the similarity of patches, the patch standard deviation is taken to discriminate flat area and edges, which can capture local geometric structures. It gets rid of the effect of highly dissimilar image patches by setting the weights to zero. Then, we add multi-kernel weights to denoising filter. Different kernel parameters are used to remove complicated noise. The experimental results show that the proposed method has superior performance to existing approaches in terms of noise suppression and detail preservation, especially for the case of low-signal-to-noise ratio (SNR). As our future research work, we intend to apply the method to speech and other intelligent recognition system.

关键词： Non-local means Multi-kernel Gaussian noise Adaptive filter

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SPCSE: soft Positive Enhanced Contrastive Learning for Sentence Embeddings 27th

SPCSE: Soft Positive Enhanced Contrastive Learning for Sent...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Liu, Lingen Chen, Zixin Chen, Guang Institute of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783031781889

Unsupervised contrastive learning for high-quality sentence representations has gained widespread attention in recent years. However, existing dropout-based data augmentation method, such as Unsup-SimCSE [13], may suffer from the limitation of minimal semantic changes, which can result in the potential exclusion of positive samples and thus hinder alignment. To alleviate this problem, we propose a novel approach called soft Positive Contrastive Sentence Embeddings (SPCSE), which leverages soft positives generated from diverse discrete data augmentation methods. By incorporating soft positives, SPCSE aims to enhance the alignment between positive samples and anchors in the representation space. Our experimental results across seven Semantic Textual Similarity (STS) tasks demonstrate that SPCSE can significantly improve the alignment of positive samples and achieve overall performance enhancement compared to Unsup-SimCSE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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An Improved soft-CBAM-YoloV5 Algorithm for Fruits and Vegetables Detection and Counting 6

An Improved Soft-CBAM-YoloV5 Algorithm for Fruits and Vegeta...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Luo, Qian Zhang, Zhen Yang, Cuimei Lin, Junran South China Normal University Department of Soft Engineering Foshan China Huizhou University Department of Soft Engineering Huizhou China

ISBN: (纸本)9798350325485

The quality of people's life has been improved as science and technology keep progressing, and artificial intelligence has been implemented into more and more fields these days. Among them, many researchers have paid attention to the scenes of quoting prices and settling accounts for fruits and vegetables in supermarkets and manual classification of fruits and vegetables in farmers' markets. In the traditional YOLOV5 method, when the same kind of fruits and vegetables are blocked from each other, the counting will be inaccurate, resulting in missed detection. In addition, the environment where different objects stay will affect the feature extraction of objects. In order to reduce the occurrence of missed detections as much as possible and improve the detection performance of the model, this paper proposes a new detection model counting method named soft-CBAM-YOLOV5, by reasonably reducing the score of adjacent detection boxes and introducing the attention mechanism CBAM (Convolutional Block Attention Module) to prevent the occurrence of missed detection and strengthen the focus on the detection target to improve the feature extraction ability of the algorithm. Compared with traditional YOLOV5, the mAP value of soft-CBAM-YOLOV5 increases from 97.05% to 97.71%. Consequently, the soft-CBAM-YOLOV5 model has greater detection performance, robustness and lower missed detection rate. © 2023 IEEE.

关键词： Feature extraction

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