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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Lohith, N. Adnan Raqeeb, S. Sai Manoj Reddy, Poreddy Sunil Kumar, Chekuri Venkata Anand Kumar, M. Chakravarthi, Bharathi Raja Department of Information Technology National Institute of Technology Karnataka Surathkal India Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Galway Ireland

ISBN: (纸本)9783031584947

Everyone now uses social media to communicate information and ideas, which has become a requirement for everyone. Social media posts can offend people or be helpful or educational for them. Memes are one type of media that is disseminated in this way through direct messages, videos, or photographs. A meme is an image or video that captures the opinions and sentiments of a particular group of people. Memes can be trolling or not, and they include an image and text that express an emotion. Most memes are unique to a particular linguistic group of people. The most frequent cause of this is different languages. Memes must be categorized since studying them can help us understand the interests of each user. As both are necessary for interpretation, the meme cannot be considered acceptable if we only gaze at the text or the image. In this article, we provided a method for categorizing memes in Telugu that considers both the meme’s text and visual features, classifies trolling and non-trolling memes, attempt to determine the emotion the meme conveys and categorizes the meme according to the domain it belongs to. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Social networking (online)

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Recent Advances on the Corrosion Resistance of Low-Density Steel:A Review

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Acta Metallurgica Sinica(English Letters) 2022年第7期35卷 1055-1067页

作者： Chao Liu Yilun Li Xuequn Cheng Xiaogang Li Institute of Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China

This paper reviews the previous literature on the alloy composition design of low-density steel(LDS),focusing on the eff ect of Al,Mn,Ni,and other alloy elements on the formation of the steel matrix and second phase,and provides classifi *** microstructure of LDS after processing includes the matrix structure,к-carbide,and B2(FeAl,NiAl,or MnAl)phase of ferritic LDS,austenitic LDS,and dual-phase *** infl uence of alloy elements on the corrosion resistance of LDS is derived from the addition of Al and Mn for ***,the infl uence of Cr and Mo addition on the corrosion resistance improvement was *** electrochemical properties of the corrosion process in LDS are ***,the microstructure of LDS aff ects the corrosion resistance properties including pitting corrosion,hydrogen embrittlement,and SCC(stress corrosion cracking).Finally,future research directions are proposed.

关键词： Low-density steel Alloy element adjustment Microstructure Corrosion resistance

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Signs to Speech 2

Signs to Speech

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vardhan, Harsh Harsavardhan, S. Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

This research introduces an innovative Sign Language to Speech Conversion Model using Convolutional Neural Networks (CNNs) to address communication barriers for the people who are deaf and unable to hear properly. The model employs deep learning for the automatic extraction of spatial patterns from sign language images. A diverse sign language dataset is carefully curated, undergoing Pre-processing for improved quality. The CNN architecture, designed for adaptability, captures local and global features, enabling accurate *** the training phase, the model learns to map sign language gestures to spoken language representations. Performance evaluation methods which include accuracy, recall, precision and F1 score, demonstrate the effectiveness of the model. Results underscore the significance for the deaf community. The discussion explores broader implications in education, healthcare, and social interactions, acknowledging limitations and proposing future research *** conclusion, the Sign Language to Speech Conversion Model contributes to sign language recognition, offering an advanced solution for communication barriers and promoting inclusivity in diverse social settings. © 2024 IEEE.

关键词： Convolution

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Deep Learning-Based Emotion Recognition using CNN, LSTM for Multimodal Text, Speech, and Facial Analysis 5

Deep Learning-Based Emotion Recognition using CNN, LSTM for ...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Firdou, Talat Binti Ashra, Mohammed Kalpan, A.V. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350389609

Emotion detection is a critical field in AI as well as computer vision, seeking to recognize or detect human emotions from facial expressions. Although this area has been greatly advanced through Convolutional Neural Networks (CNNs), current methods using only single-modal data often exhibit reduced accuracy. In this work, we confront these challenges with a hybrid deep learning method for disparate modalities of information such as speech, video, and text. We pre-process and then use CNN feature extractors for the FER2013 data set. The hybrid deep learning model, hence proposed, achieved a weighted accuracy of 79.3% on FER2013, overcoming the conventional unimodal methods. We are using transformers library with the pre-trained RoBERTa model for text. For speech emotion features, we are using TESS dataset then using a CNN and LSTM (Long Short-Term Memory) model. The CNN extracts feature from the speech, while the LSTM is used for temporal analysis to detect emotions. Once these multimodal features have been obtained, our work would be to fuse in order to get the final emotion recognition using Deep Neural Networks (DNNs). The process increases the precision and reliability of the recognition systems, helping to accumulate even more detailed intuition for different emotional states for a combined approach. © 2024 IEEE.

关键词： Convolutional neural networks

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Violence Detection System using MobileNetV2 3

Violence Detection System using MobileNetV2

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350375190

The number of cases of violence and fights has been increasing around the world. With the use of CCTV, such incidents can be recorded but the detection of Violence is a major issue around the globe, and it plays a vital role in the safety and security of the area. It helps in effective surveillance and law enforcement. The effectiveness of any violence detection system depends on the speed and accuracy with which it detects the events and how it performs over different types of videos of varied sizes and formats. In the proposed algorithm, the dataset used contains videos of different types and scenarios to help the model understand and train better. To improve the overall effectiveness of the model, Lightweight CNN MobileNetV2 has been used along with that Bidirectional LSTM (BiLSTM) has been used. The accuracy rate of the proposed model is 98%. © 2024 IEEE.

关键词： Long short-term memory

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Handy Skin Diseases using Deep Learning to detect Skin Infections While on the Go 2

Handy Skin Diseases using Deep Learning to detect Skin Infec...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Saveetha, R. Nithya, M. Satheeshkumar, S. Dakshana, M. Gunalan, K. Anandan, P. Bannari Amman Institute of Technology Department of Computer Technology Tamilnadu India Bannari Amman Institute of Technology Department of Artificial intelligence and data science Tamilnadu India Sathyabama Institute of Science and Technology Department of Physics Tamilnadu India

ISBN: (纸本)9798350368413

Skin diseases can significantly impact a person's quality of life. Accurate and timely diagnosis is crucial for effective treatment. This research explores the application of Convolutional Neural Networks (CNNs) to improve dermatological diagnosis. The study utilizes the ISIC 2017 dataset, a large collection of dermatoscopic images, to train and evaluate various CNN architectures, including VGG-16, AlexNet, DermNet, GoogleNet, and MobileNetV2. Preprocessing techniques are employed to enhance image quality and facilitate accurate classification. The proposed hybrid model, combining DermNet and MobileNet with Softmax and ReLU activation functions, achieves an impressive accuracy of 100% in classifying skin diseases. This result demonstrates the potential of deep learning techniques to revolutionize dermatological diagnosis, enabling early detection and appropriate treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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High-Dimensional Volatility Matrix Estimation with Cross-Sectional Dependent and Heavy-Tailed Microstructural Noise

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Journal of Systems science & Complexity 2023年第5期36卷 2125-2154页

作者： LIANG Wanwan WU Ben FAN Xinyan JING Bingyi ZHANG Bo Center for Applied Statistics School of StatisticsInstitute of Probability and StatisticsRenmin University of ChinaBeijing 100086China Department of Statistics&Data Science Southern University of Science and TechnologyShenzhen 518055China

The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial ***,most existing studies have been built on the sub-Gaussian and cross-sectional independence assumptions of microstructure noise,which are typically violated in the financial *** this paper,the authors proposed a new robust volatility matrix estimator,with very mild assumptions on the cross-sectional dependence and tail behaviors of the noises,and demonstrated that it can achieve the optimal convergence rate n-1/***,the proposed model offered better explanatory and predictive powers by decomposing the estimator into low-rank and sparse components,using an appropriate regularization *** studies demonstrated that the proposed estimator outperforms its competitors under various dependence structures of microstructure ***,an extensive analysis of the high-frequency data for stocks in the Shenzhen Stock Exchange of China demonstrated the practical effectiveness of the estimator.

关键词： Cross-sectional dependence high-dimensional data high-frequency data integrated volatility matrix market microstructure noise

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Multi-modal Sentiment Analysis Using Text and Audio for Customer Support Centers 1

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International Conference on Advances in Communication technology and Computer Engineering, ICACTCE 2023

作者： Srivastava, Hardik Sunil, Sneha Shantha Kumari, K. Kanmani, P. Data Science and Business Systems SRM Institute of Science and Technology Chennai India Networking and Communications SRM Institute of Science and Technology Chennai India

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

Customer service has become crucial for every organization looking to grow and improve its clientele in today’s cutthroat marketplace. Companies cannot afford to fall short of consumer expectations. With the recent progress in Artificial Intelligence, companies have adopted AI techniques like sentiment analysis to measure customer satisfaction. Chatbots are the foundation for most AI applications in call centers trained for either question-answering tasks or calculating sentiment out of user feedback via surveys. These existing expert systems only utilize text mining techniques to classify sentiment as positive or negative. In this paper, we propose a Multimodal learning framework for tackling the sentiment classification task, employing acoustic and linguistic modalities from real-world conversations between support representatives and customers and survey feedback data using the decision-level fusion technique. Leveraging the classification abilities and feature representations from both modalities, our model achieves the best results amongst all implementations and enables us to better analyze the sentiment expressed by the user than using a single modality like text or audio. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Deepfake Detection System 2

Deepfake Detection System

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Shrivas, Shaswat Rai, Aditya Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

Detecting deepfake content presents a formidable challenge, necessitating advanced methodologies. This paper proposes a holistic strategy employing Facenet-pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection. Facenet-pytorch serves as the cornerstone for facial feature recognition, while MTCNN efficiently identifies faces in images during preprocessing. InceptionResnetV1 scrutinizes visual details to detect subtle anomalies indicative of manipulation, such as unnatural facial expressions and incongruent lip synchronization. Our approach underscores the importance of maintaining a delicate balance between accurate detection and individual privacy rights. By leveraging these advanced models, we achieve significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies. © 2024 IEEE.

关键词： Biometrics

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Brain Tumor Detection & Classification into Different Categories using Deep Learning Model 3

Brain Tumor Detection & Classification into Different Catego...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Tambe, Utkarsh Yashwant Shanthini, A. Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350348347

This study's objective is to create a Convolutional Neural Network (CNN) based system for identifying and categorizing brain cancers from MRI scans. The proposed method's accuracy was assessed and contrasted with other classification techniques. The difficulty of identifying and classifying tumors is a result of the perplexing nature of images produced by imaging methods like MRI. In this study, a deep learning model for classifying various tumor types into 4 categories/types - glioma, meningioma, pituitary and no-tumor is proposed. To determine the most effective course of treatment, a special model with hyperparameter adjustment was built. The suggested method's effectiveness in accurately classifying the tumors visible in the MRI pictures was demonstrated after it was applied to a sizable dataset of MRI images with an accuracy of 95.94%. © 2023 IEEE.

关键词： Magnetic resonance imaging

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