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Emotion Recognition From Speech Using Telugu Language and Deep Learning Models

Emotion Recognition From Speech Using Telugu Language and De...

引用

Communications and computer science (InCCCS), International Conference on

作者： Aditya S Vaka Satwik R Vamsi Krishna V Vimal C Suja Palaniswamy Peeta Basa Pati Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

This research involves the use of deep learning techniques to detect emotions, in Telugu speech signals, a research domain that has received little attention. Amrita speech emotion dataset-1 (ASED-1) consisting of 550 signals representing five categories. Surprise, anger, happiness, neutrality, and sadness. We explore how well various deep learning models can recognize emotions from speech. Our approach involves training models such as Convolutional Neural Networks (CNNs) Long Short-Term Memory (LSTM) networks and Multi-Layer Perceptron (MLPs) on a portion of the data and evaluating their performance on a test set. The MLP model achieved an accuracy rate of 84% although this was constrained by the size of the dataset. The CNN model showed results with decreasing trends in both training and validation loss indicating learning and generalization abilities. Similarly, the AlexNet architecture demonstrated a decline in both training and validation loss suggesting a fit without overfitting. Additionally, we included a machine learning classifier at the layer. Observed different accuracies across various models. The RNN model achieved an encouraging accuracy rate on the test data showing potential for emotion classification tasks but also room for improvement, through complex architectures or larger datasets. This research paper adds to the knowledge, in the field of emotion recognition, in Telugu speech by emphasizing the difficulties and possibilities of using deep learning techniques in this domain.

关键词： Training Deep learning Emotion recognition Technological innovation Accuracy Speech recognition computer architecture

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Emotion and Cause Detection in Dialogues Using Capsule Networks

Emotion and Cause Detection in Dialogues Using Capsule Netwo...

引用

Women in computing (InCoWoCo), International Conference for

作者： Shyam Ganesh K Lavanya G S Baghavathi Priya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Identifying the sources of emotions expressed in text-to-text conversational dialogs significantly contributes to improving applications such as virtual assistants, customer support, and interventions aimed at improving mental well-being. Traditional approaches to emotion analysis are primarily focused on classification procedures rather than consideration of the underlying causes responsible for triggering specific emotional behaviors in various contexts. This paper has presented a new model that identifies emotions in conversational data but simultaneously enables the determination of the root causes of such emotions. The framework uses DistilBERT to generate subtle contextual embeddings, which incorporate the richness of meaning for each utterance. For that purpose, it uses a Capsule Network for the efficient modeling of sequential dependencies and hierarchical relationships that exist in dialog. This altogether makes this system decode more complex emotional triggers with greater accuracy. Furthermore, the model is designed to be scalable and efficient, meaning it can comfortably be deployed in any real-time environment with minimal computational resources. The Experimental Results, integrated A two-step model consisting of Distil BERT and Capsule Network greatly enhances the accuracy of emotion cause detection and can be a strong solution for applications requiring minute emotions and causal inferences from conversational data.

关键词： Emotion recognition Accuracy Computational modeling Virtual assistants Mental health Data models Real-time systems Computational efficiency Monitoring

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Grad-CAM-Based Fine-Grained Classification for Celestial Object

Grad-CAM-Based Fine-Grained Classification for Celestial Obj...

引用

Communication, Control and Intelligent Systems (CCIS)

作者： Devansh Handa Kindi Krishna Nikhil S Duvarakanath Kanaganandini Kanagaraj Suja Palaniswamy Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

This paper proposes a new method of classifying celestial objects using Grad-Class Activation Mapping (Grad-CAM) in conjunction with deep learning models such as VGG16, Google Net, Dense Net, and Efficient Net. The paper looks at the difficulties characterizing the enormous varieties of astronomical objects and the complexity that makes it difficult to classify the objects by conventional methods. From the empirical evaluation, it is clear that the Dense Net model has the ability to predict the right class of celestial objects with the help of Grad-CAM and has the highest accuracy of 99.76% for differentiating between images in the case of nebula. Grad-CAM works well at identifying the features that affect the model’s output. Thus, the integration of Grad-CAM with deep learning helps to increase the classification indicators and offers useful information regarding the morphological and spectral properties of the objects, thus, contributing to the further development of observational astronomy. It outperformed Class Activation Mapping (CAM) in terms of performance.

关键词： Deep learning Accuracy Predictive models Control systems Internet Complexity theory Intelligent systems Astronomy

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On further improvement of data look-up latency in Residue Class (RC) based Peer-2-Peer networks 39th

On further improvement of data look-up latency in Residue Cl...

引用

39th International Conference on computers and Their Applications, CATA 2024

作者： Roy, Indranil Mitra, Reshmi Kaluvakuri, Swathi Rahimi, Nick Gupta, Bidyut Department of Computer Science Southeast Missouri State University Cape GirardeauMO United States Enterprise Fleet Management St. LouisMO United States School of Computing Sciences and Computer Engineering University of Southern Mississippi HattiesburgMS United States School of Computing Southern Illinois University CarbondaleIL United States

In this work, the existing Residue-class based peer-to peer (P2P) network has been considered because of its manifold advantages. Two of the most prominent advantages relevant to the present work are: (1) all peers with the same interest (or possessing same resource type) structurally form a group of diameter one, and (2) the group heads are connected in the form of a ring and the ring always remains connected even in presence of any churn. However, data look-up latency is n/2 for an n group-network. It may become substantial if n is large. To improve the latency, some topological properties of Star inter-connection network have been used to modify the existing RC based network appropriately resulting in remarkable improvement of latency. © 2024, EasyChair. All rights reserved.

关键词： Stars

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Building a Secure and Scalable Finance Chatbot: Client-Server Architecture, Load Balancing, and TLS Security

Building a Secure and Scalable Finance Chatbot: Client-Serve...

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Sustainable Expert Systems (ICSES), International Conference on

作者： T. Anisha Reddy S Akhilesh Sodum Manogna Sreebha Bhaskaran Kavitha C.R. Dept of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research introduces a new and improved model for a financial chatbot based on deep learning, machine learning, and natural language processing to provide suitable financial services and increase the financial literacy of users. It utilizes a detailed financial database, another complex machine learning structure for market forecasts., and natural language processing to parse user questions. Moreover, there is the customer resource management feature that applies machine learning in the analysis of financial data and natural language processing for understanding the user”s intent. The paper also looks into the use of transport layer security (TLS) encryption to secure the flow of the sensitive information, the data that is to be transmitted from the chatbot server to the user clients. By integrating the dimensions of concepts such as financial analysis, personal finance advice, and data transmission, this research provides an extensive and secure approach to improving one”s financial situation.

关键词： Finance Training data Chatbots Load management Encryption Security Servers Resource management Reliability Protection

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Predictive Analytics for Anticipating Adverse Drug Reactions to ameliorate Drug Development and Prescription Practices

Predictive Analytics for Anticipating Adverse Drug Reactions...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Ch. Aneesh Gembali Saumik K.V.V. Varun Rajesh M Sreevidya B Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Integrating Artificial Intelligence into Health Care involves employing AI technologies and Algorithms to reinforce various facets of Medical Domain including discovery of the drugs and its subsequent development. However, with the advent of various drugs for several medical conditions, comes the side effects. This work aims to present a comprehensive approach to drug side effect prediction using a machine learning algorithm for Drug organization and analysis of extensive drug-related datasets. Utilizing advanced data-driven methodologies techniques, the system aims to predict potential side effects early in the drug development lifecycle. The repository used serves as a crucial infrastructure for managing diverse data, including information on drugs, chemical properties, and historical side effect records. The main objective of this research work is to enhance patient safety by minimizing adverse drug reactions and reducing treatment discontinuation rates, thereby mitigating the economic impact on pharmaceutical companies. The system enables informed decision-making throughout the drug development process by empowering researchers, healthcare practitioners, and regulatory bodies with an integrated decision-support the DBMS, acting as the backbone for the development of this predictive model. The proposed model integrated into drug development, aims to revolutionize drug safety practices, offering a proactive and data-driven approach. The predicted outcomes have broader implications for advancing healthcare technologies, optimizing drug development practices, and contributing to more efficient and informed pharmaceutical sciences.

关键词： Drugs Machine learning algorithms Medical conditions Precision medicine Machine learning Predictive models Prediction algorithms

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A Comparative Analysis of Deep Learning Models for Detection of Diabetic Foot Ulcer using Foot Thermography Images

A Comparative Analysis of Deep Learning Models for Detection...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Chava Sriya Chowdary Dovala Ruthwik Kancharla Nitish Sai Suthapalli Rishik Nalini Sampath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Diabetic foot ulcers (DFUs) are a serious consequence for diabetes individuals, frequently resulting in amputation. Early identification is critical in averting such results. This study looks at the effectiveness of deep learning models in recognizing DFUs from foot thermography photos. We evaluate the performance of several deep learning architectures against more contemporary models. The models will be trained and assessed using a publicly accessible foot thermography picture collection that includes both healthy and DFU patients. Each model’s performance in DFU identification will be evaluated based on accuracy, precision, recall, and $\mathbf{f 1}$ score. We intend to find the deep learning model with the greatest accuracy,f1 score, and robustness in DFU classification, which would pave the way for a computer-aided diagnostic tool that uses thermography to detect DFU in diabetes patients early. This research shows potential for enhancing patient care and lowering amputation rates.

关键词： Deep learning Accuracy Computational modeling Training data Infrared imaging User interfaces Transformers Data models Diabetes Complexity theory

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Aural Signatures: Audio Fingerprinting Techniques for Real-Time Audio Recognition

Aural Signatures: Audio Fingerprinting Techniques for Real-T...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： B. Sharan Nripesh Reddy B. Sai Venkat I. Manohar S Abhishek Anjali T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Audio fingerprinting is an essential technique for copyright protection and music recognition. The capacity to accurately categorize audio content and spot possible copyright violations is vital for musicians, content creators, and streaming services. With its valuable solutions for copyright protection, content monitoring, and music recommendation, this study has significant implications for the music industry. In the research, the research focused on developing robust audio fingerprinting methods for precise audio content recognition. These tactics have shown to be remarkably accurate and quite effective. The approach is based on the use of strong hashing techniques and the extraction of various audio aspects. Long Short-Term Memory (LSTM) along with SVM, KNN, and CNN were employed in the research. Notably, LSTM outperformed other models, showcasing an outstanding accuracy rate of 93 %. The results hold particular promise for massive music platforms and instantaneous content matching. The proposed methods have demonstrated the effectiveness of these strategies in identifying audio material, even under challenging conditions.

关键词： Deep learning Accuracy Music Fingerprint recognition Copyright protection Real-time systems Data models

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TweetFeel: Analyzing Emotions in the Twittersphere

TweetFeel: Analyzing Emotions in the Twittersphere

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Adithya S Gokulan Mahati Reddy M Varshith Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The majority of people on the planet now have access to the Internet for text, image, audio, and video communication. Through social media, people from many backgrounds share knowledge about current events and express their own opinions on them. Analyzing people’s emotions is necessary to comprehend and identify the behavior of such vast amounts of textual data about them. The study focuses on information gathered from Twitter, one of the most widely used social media platforms, by examining both historical and real-time feeds and extracting emotions from them. The necessary English-language Twitter data is transformed into a vector of six emotions, and supervised learning methods like Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest, Gradient Boosting, and Multi-Layer Perceptron are utilized to identify the label of one of the basic human emotions. Gradient Boosting and SVM are the top-performing models that reported the highest F1-Score of 0.88 among the other models. Sadness and joy came out to be the best-performing emotions whereas it was comparatively difficult to predict surprise emotion.

关键词： Support vector machines Knowledge engineering Logistic regression Social networking (online) Planets Blogs Boosting

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UZNER: A Benchmark for Named Entity Recognition in Uzbek 12th

UZNER: A Benchmark for Named Entity Recognition in Uzbek

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12th National CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Yusufu, Aizihaierjiang Jiang, Liu Ainiwaer, Abidan Teng, Chong Yusufu, Aizierguli Li, Fei Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China School of Computer Science and Technology Xinjiang Normal University Urumqi China

ISBN: (纸本)9783031446924

Named entity recognition (NER) is a key task in natural language processing, and entity recognition can provide necessary semantic information for many downstream tasks. However, the performance of NER is often limited by the richness of language resources. For low-resource languages, NER usually performs poorly due to the lack of sufficient labeled data and pre-trained models. To address this issue, we manually constructed a large-scale, high-quality Uzbek NER corpus of Uzbek, and experimented with various NER methods. We improved state-of-the-art baseline models by introducing additional features and data translations. Data translation enables the model to learn richer syntactic structure and semantic information. Affix features provide knowledge at the morphological level and play an important role in identifying oversimplified low-frequency entity labels. Our data and models will be available to facilitate low-resource NER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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