Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212362
Anuj Mangal, Anuj Kumar
Twitter has become a popular platform for people to share their thoughts and opinions with the world. It allows users to post openly on any topic, giving them the freedom to express themselves without fear of judgment or censorship including those relevant to throat cancer. Twitter sentiment analysis is an important tool for understanding the relative sentiment of the public for certain topics or ideas present on the platform. By using Natural Language Processing (NLP) techniques on millions of tweets, Sentiment Analysis determines how likely each tweet falls into a pre-defined positive or negative classification. The tweets will be classified into three categories using the Lexicon, CNN, LSTM, and CNN-LSTM: positive, neutral, and negative. This study examined the use of text tweets from Twitter as a source of data. Curated tweets from public accounts were utilized and a total of 30002 tweets were collected. The study suggests that the use of Lexicon, CNN, LSTM, and CNN-LSTM approaches can enhance accuracy when conducting a classification task. Through this process, 82% accuracy has been obtained with 24000 positive tweets and 6000 negative tweets.
{"title":"Deep Learning based Opinion Mining on Throat Cancer Social Media Posts","authors":"Anuj Mangal, Anuj Kumar","doi":"10.1109/ICECAA58104.2023.10212362","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212362","url":null,"abstract":"Twitter has become a popular platform for people to share their thoughts and opinions with the world. It allows users to post openly on any topic, giving them the freedom to express themselves without fear of judgment or censorship including those relevant to throat cancer. Twitter sentiment analysis is an important tool for understanding the relative sentiment of the public for certain topics or ideas present on the platform. By using Natural Language Processing (NLP) techniques on millions of tweets, Sentiment Analysis determines how likely each tweet falls into a pre-defined positive or negative classification. The tweets will be classified into three categories using the Lexicon, CNN, LSTM, and CNN-LSTM: positive, neutral, and negative. This study examined the use of text tweets from Twitter as a source of data. Curated tweets from public accounts were utilized and a total of 30002 tweets were collected. The study suggests that the use of Lexicon, CNN, LSTM, and CNN-LSTM approaches can enhance accuracy when conducting a classification task. Through this process, 82% accuracy has been obtained with 24000 positive tweets and 6000 negative tweets.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"549 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123135396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212400
B. Kannan, P. Solainayagi, H. Azath, Subbiah Murugan, C. Srinivasan
Real-time monitoring and analysis of data from various sources have been made possible due to the growth of Internet of Things (IoT)-enabled embedded systems in military applications. This has allowed improved situational awareness and the identification of potential threats. However, it is essential that the communication between these systems to be protected against illegal access and intervention to maintain their integrity. This research study investigates whether or not it is possible to have encrypted communication in embedded systems that make use of the IoT for military purposes. It gives an overview of the many security protocols and algorithms that may be used to secure communication, along with the problems and constraints that such protocols and algorithms provide. Case studies and examples of how secure transmission has been deployed in real-world military applications are covered along with the lessons learned and best practices for moving forward with the technology's development. This article emphasizes the significance of encrypted communication in military applications and its role in protecting the safety and security of soldiers and equipment.
{"title":"Secure Communication in IoT-enabled Embedded Systems for Military Applications using Encryption","authors":"B. Kannan, P. Solainayagi, H. Azath, Subbiah Murugan, C. Srinivasan","doi":"10.1109/ICECAA58104.2023.10212400","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212400","url":null,"abstract":"Real-time monitoring and analysis of data from various sources have been made possible due to the growth of Internet of Things (IoT)-enabled embedded systems in military applications. This has allowed improved situational awareness and the identification of potential threats. However, it is essential that the communication between these systems to be protected against illegal access and intervention to maintain their integrity. This research study investigates whether or not it is possible to have encrypted communication in embedded systems that make use of the IoT for military purposes. It gives an overview of the many security protocols and algorithms that may be used to secure communication, along with the problems and constraints that such protocols and algorithms provide. Case studies and examples of how secure transmission has been deployed in real-world military applications are covered along with the lessons learned and best practices for moving forward with the technology's development. This article emphasizes the significance of encrypted communication in military applications and its role in protecting the safety and security of soldiers and equipment.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125123094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ethereum, a leading cryptocurrency, is advancing the way we handle digital transactions. One important concept in Ethereum is “account abstraction,” which could play a crucial role in making it accessible to a wider audience. Currently, Ethereum has two types of accounts: externally owned accounts (EOAs) and contract accounts (CAs). EOAs are controlled by a public address and private key, allowing users to initiate transactions and interact with smart contracts. However, losing the private key can result in the loss of funds, and EOAs are not quantum safe. On the other hand, CAs are controlled by the code written on the Ethereum Virtual Machine (EVM) and do not possess private keys. They rely on network storage, and their creation incurs a cost. To enhance the existing technology and make it more user-friendly, there is a proposal for “account abstraction” in the Ethereum protocol. This change would enable users to interact with smart contracts using smart contract wallets instead of externally owned accounts. This would bring greater flexibility and security to the management of user accounts, as well as open doors for new and innovative user experiences.
{"title":"Account Abstraction via Singleton Entrypoint Contract and Verifying Paymaster","authors":"Aniket Kumar Singh, Inzimam Ul Hassan, Gaganjot Kaur, Shanu Kumar, Anmol","doi":"10.1109/ICECAA58104.2023.10212316","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212316","url":null,"abstract":"Ethereum, a leading cryptocurrency, is advancing the way we handle digital transactions. One important concept in Ethereum is “account abstraction,” which could play a crucial role in making it accessible to a wider audience. Currently, Ethereum has two types of accounts: externally owned accounts (EOAs) and contract accounts (CAs). EOAs are controlled by a public address and private key, allowing users to initiate transactions and interact with smart contracts. However, losing the private key can result in the loss of funds, and EOAs are not quantum safe. On the other hand, CAs are controlled by the code written on the Ethereum Virtual Machine (EVM) and do not possess private keys. They rely on network storage, and their creation incurs a cost. To enhance the existing technology and make it more user-friendly, there is a proposal for “account abstraction” in the Ethereum protocol. This change would enable users to interact with smart contracts using smart contract wallets instead of externally owned accounts. This would bring greater flexibility and security to the management of user accounts, as well as open doors for new and innovative user experiences.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121529887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212166
N. P, M. V, C. Rupesh, B. Kartheek, Y. Lekhya, K. Swetha
This study suggests a wearable gadget with an autonomous fall detector that can lower risks by identifying falls and notifying care takers right away. This research study combines a heart-rate sensor and an accelerometer to create a user-adaptive fall detection system based on cluster analysis. The suggested fall detector seeks to achieve high accuracy using a simple model under a variety of circumstances. Additionally, this research study tests the efficiency of the cluster-analysis-based anomaly identification as well as the performance improvement of combining a heart rate sensor and an accelerometer. This study also demonstrates the utility of the user-adaptive approach when using both acceleration and heart rate inputs. The system will alert the carer through GSM if the user's orientation data values become aberrant in any way. The system design takes into account a straightforward, inexpensive, and power-efficient design.
{"title":"A Robust Recession Detective Analysis System using IoT Smart Sensor Devices","authors":"N. P, M. V, C. Rupesh, B. Kartheek, Y. Lekhya, K. Swetha","doi":"10.1109/ICECAA58104.2023.10212166","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212166","url":null,"abstract":"This study suggests a wearable gadget with an autonomous fall detector that can lower risks by identifying falls and notifying care takers right away. This research study combines a heart-rate sensor and an accelerometer to create a user-adaptive fall detection system based on cluster analysis. The suggested fall detector seeks to achieve high accuracy using a simple model under a variety of circumstances. Additionally, this research study tests the efficiency of the cluster-analysis-based anomaly identification as well as the performance improvement of combining a heart rate sensor and an accelerometer. This study also demonstrates the utility of the user-adaptive approach when using both acceleration and heart rate inputs. The system will alert the carer through GSM if the user's orientation data values become aberrant in any way. The system design takes into account a straightforward, inexpensive, and power-efficient design.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123393057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212417
Muchai Jemimah, M. Kuruvilla, Masato Gunji, P. Prasad, Dr. G. Jaspher, Associate W Kathrine, Mr S. Kirubakaran, Mercedes Evangelina
The implementation of Industry 4.0 will assist the healthcare sector in building reliable and sustainable supply chains. This research discusses about how the current technologies can lead to improve the existing supply chain management systems and how the drugs- one of the main component circulated in the supply chain be safeguarded from being replaced by counterfeits. As a result, this research study proposes a centralized system, which includes gathering and sending purchase orders, tracking the transportation of drugs, implementing a programme for drug exchanges in collaboration with numerous hospitals, and using AI models for demand forecasting to guard against shortage or oversupply conditions. To prevent an inflow of counterfeit supplies, blockchain technology is used to monitor pharmaceuticals and other medical products. To monitor the state of pharmaceutical products in storage facilities or warehouses, IIoT is employed. This improvement guarantees improved healthcare facilities and more transparency in the procurement procedures.
{"title":"Implementation of Industry 4.0 in Supply Chain Management in the Healthcare Industry","authors":"Muchai Jemimah, M. Kuruvilla, Masato Gunji, P. Prasad, Dr. G. Jaspher, Associate W Kathrine, Mr S. Kirubakaran, Mercedes Evangelina","doi":"10.1109/ICECAA58104.2023.10212417","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212417","url":null,"abstract":"The implementation of Industry 4.0 will assist the healthcare sector in building reliable and sustainable supply chains. This research discusses about how the current technologies can lead to improve the existing supply chain management systems and how the drugs- one of the main component circulated in the supply chain be safeguarded from being replaced by counterfeits. As a result, this research study proposes a centralized system, which includes gathering and sending purchase orders, tracking the transportation of drugs, implementing a programme for drug exchanges in collaboration with numerous hospitals, and using AI models for demand forecasting to guard against shortage or oversupply conditions. To prevent an inflow of counterfeit supplies, blockchain technology is used to monitor pharmaceuticals and other medical products. To monitor the state of pharmaceutical products in storage facilities or warehouses, IIoT is employed. This improvement guarantees improved healthcare facilities and more transparency in the procurement procedures.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124197893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212399
Vinay Nagarad Dasavandi Krishnamurthy, S. Degadwala, Dhairya Vyas
This research article presents a data-driven approach for predicting future sea level rise using climate data analysis. By employing advanced statistical techniques and machine learning algorithms, the study establishes correlations between historical climate variables and observed sea level rise. Ensemble modeling techniques are utilized to explore uncertainties and generate multiple simulations, offering a range of potential outcomes. The findings provide valuable insights for policymakers and coastal communities, enabling informed decision-making and the development of effective strategies to address the challenges posed by rising sea levels. Overall, this research contributes to the field of climate science by providing a robust framework for predicting sea level rise and preparing for its impacts in a changing climate.
{"title":"Forecasting Future Sea Level Rise: A Data-driven Approach using Climate Analysis","authors":"Vinay Nagarad Dasavandi Krishnamurthy, S. Degadwala, Dhairya Vyas","doi":"10.1109/ICECAA58104.2023.10212399","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212399","url":null,"abstract":"This research article presents a data-driven approach for predicting future sea level rise using climate data analysis. By employing advanced statistical techniques and machine learning algorithms, the study establishes correlations between historical climate variables and observed sea level rise. Ensemble modeling techniques are utilized to explore uncertainties and generate multiple simulations, offering a range of potential outcomes. The findings provide valuable insights for policymakers and coastal communities, enabling informed decision-making and the development of effective strategies to address the challenges posed by rising sea levels. Overall, this research contributes to the field of climate science by providing a robust framework for predicting sea level rise and preparing for its impacts in a changing climate.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127999113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212161
Roshni Padate, M. Kalla, Ashutosh Gupta, Arvind Sharma
This study presents a comprehensive review of the use of federated learning in the context of image captioning in distributed environments. It focuses on key aspects such as privacy preservation, data locality, and collaborative model training. The evolution of federated learning and its unique characteristics are explored, along with an examination of available open-source frameworks specific to image captioning. The study categorizes different approaches to federated learning for image captioning and showcases recent applications in diverse domains, including medical imaging, edge computing, autonomous vehicles, social media, and cross-domain image analysis. Additionally, optimization techniques, security analysis, and research challenges are discussed, encompassing data heterogeneity, privacy preservation, communication efficiency, limited labeling, scalability, and robustness against adversarial attacks. This comprehensive review contributes to a deeper understanding of federated learning for image captioning and highlights areas for further research and advancement in the field.
{"title":"Federated Learning for Image Captioning: A Comprehensive Review of Privacy-Preserving Collaborative Model Training in Distributed Environments","authors":"Roshni Padate, M. Kalla, Ashutosh Gupta, Arvind Sharma","doi":"10.1109/ICECAA58104.2023.10212161","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212161","url":null,"abstract":"This study presents a comprehensive review of the use of federated learning in the context of image captioning in distributed environments. It focuses on key aspects such as privacy preservation, data locality, and collaborative model training. The evolution of federated learning and its unique characteristics are explored, along with an examination of available open-source frameworks specific to image captioning. The study categorizes different approaches to federated learning for image captioning and showcases recent applications in diverse domains, including medical imaging, edge computing, autonomous vehicles, social media, and cross-domain image analysis. Additionally, optimization techniques, security analysis, and research challenges are discussed, encompassing data heterogeneity, privacy preservation, communication efficiency, limited labeling, scalability, and robustness against adversarial attacks. This comprehensive review contributes to a deeper understanding of federated learning for image captioning and highlights areas for further research and advancement in the field.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130315831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212389
Bianchi Sangma, Vandana Sharma
Natural Language Processing is a thriving branch of artificial intelligence with diverse applications across multiple domains. In recent years, advances in machine learning models for NLP tasks have resulted in a parallel development in NLP methodologies. These models are capable of performing complicated NLP tasks such language translation, sentiment analysis, text categorization, and text production. This study reviews the NLP models by analyzing the traditional models, such as rule-based systems and statistical models, and then move on to the recent neural network and deep learning models. Natural Language Processing (NLP) is a branch of artificial intelligence with diverse applications across multiple domains. In recent years, advances in machine learning models for NLP tasks have resulted in a parallel development of NLP methodologies. These models are capable of performing complicated NLP tasks such as language translation, sentiment analysis, text categorization, and text production.
{"title":"Natural Language Processing Models: A Comparative Perspective","authors":"Bianchi Sangma, Vandana Sharma","doi":"10.1109/ICECAA58104.2023.10212389","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212389","url":null,"abstract":"Natural Language Processing is a thriving branch of artificial intelligence with diverse applications across multiple domains. In recent years, advances in machine learning models for NLP tasks have resulted in a parallel development in NLP methodologies. These models are capable of performing complicated NLP tasks such language translation, sentiment analysis, text categorization, and text production. This study reviews the NLP models by analyzing the traditional models, such as rule-based systems and statistical models, and then move on to the recent neural network and deep learning models. Natural Language Processing (NLP) is a branch of artificial intelligence with diverse applications across multiple domains. In recent years, advances in machine learning models for NLP tasks have resulted in a parallel development of NLP methodologies. These models are capable of performing complicated NLP tasks such as language translation, sentiment analysis, text categorization, and text production.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128745130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212101
K. Deepa, P. Perumal, B. Mathivanan
Online Customer Reviews (OCRs) make it difficult for firms to examine them due to their number, diversity, pace, and validity. The big data analytics study predicts OCR reading and its usefulness. Titles with positive emotion and sentimental reviews with neutral polarity attract more readers. Online merchants may use this work to build scale automated processes for sorting and categorizing huge OCR data, benefiting vendors and consumers. Current OCR sorting approaches may prejudice readership and usefulness. Python crawled, processed, and displayed data using Natural Language Processing (NLP). The crawling dataset collected literature using a Pubmed Application Programming Interface (API) module. Natural Language Toolkit (NLTK) processed text data. Tokens were processed into bigrams and trigrams using n-grams. According to study abstracts, West Java has the most stunting research. Text mining and NLP may enhance oral history and historical archaeology. Text mining algorithms were intended for enormous data and public texts, making them inappropriate for historical and archaeological interpretation. Text analysis can effectively handle and evaluate vast amounts of data, which may substantially enrich historical archaeology study, especially when dealing with digital data banks or extensive texts.
{"title":"Text Extraction and Mining Methods Used in Data Science","authors":"K. Deepa, P. Perumal, B. Mathivanan","doi":"10.1109/ICECAA58104.2023.10212101","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212101","url":null,"abstract":"Online Customer Reviews (OCRs) make it difficult for firms to examine them due to their number, diversity, pace, and validity. The big data analytics study predicts OCR reading and its usefulness. Titles with positive emotion and sentimental reviews with neutral polarity attract more readers. Online merchants may use this work to build scale automated processes for sorting and categorizing huge OCR data, benefiting vendors and consumers. Current OCR sorting approaches may prejudice readership and usefulness. Python crawled, processed, and displayed data using Natural Language Processing (NLP). The crawling dataset collected literature using a Pubmed Application Programming Interface (API) module. Natural Language Toolkit (NLTK) processed text data. Tokens were processed into bigrams and trigrams using n-grams. According to study abstracts, West Java has the most stunting research. Text mining and NLP may enhance oral history and historical archaeology. Text mining algorithms were intended for enormous data and public texts, making them inappropriate for historical and archaeological interpretation. Text analysis can effectively handle and evaluate vast amounts of data, which may substantially enrich historical archaeology study, especially when dealing with digital data banks or extensive texts.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128344744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1109/ICECAA58104.2023.10212180
Siddharth Chhetri, M. Joshi, C. Mahamuni, Repana Naga Sangeetha, Tushar Roy
The paper provides a comprehensive overview of speech enhancement techniques and their applications. It discusses challenges in non-stationary noise, reverberation, and overlapping speech. Approaches like comb filtering, LPC-based filtering, and adaptive filtering, HMM filtering, Wiener filtering, ML estimation, Bayesian estimation, MMSE estimation, and transform domain methods, AI-based approaches are explored. The effectiveness and challenges of each approach are discussed. Applications in telecommunications, voice-controlled systems, hearing aids, speech recognition, and audio restoration are highlighted. The paper presents outcomes and advancements in speech enhancement. Valuable insights are provided for researchers, engineers, and practitioners in the field. The findings aid in selecting suitable techniques for improved speech quality and intelligibility.
{"title":"Speech Enhancement: A Survey of Approaches and Applications","authors":"Siddharth Chhetri, M. Joshi, C. Mahamuni, Repana Naga Sangeetha, Tushar Roy","doi":"10.1109/ICECAA58104.2023.10212180","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212180","url":null,"abstract":"The paper provides a comprehensive overview of speech enhancement techniques and their applications. It discusses challenges in non-stationary noise, reverberation, and overlapping speech. Approaches like comb filtering, LPC-based filtering, and adaptive filtering, HMM filtering, Wiener filtering, ML estimation, Bayesian estimation, MMSE estimation, and transform domain methods, AI-based approaches are explored. The effectiveness and challenges of each approach are discussed. Applications in telecommunications, voice-controlled systems, hearing aids, speech recognition, and audio restoration are highlighted. The paper presents outcomes and advancements in speech enhancement. Valuable insights are provided for researchers, engineers, and practitioners in the field. The findings aid in selecting suitable techniques for improved speech quality and intelligibility.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117087983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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