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/ICECAA58104.2023.10212389
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}
This research study aims to generate a database in verification phase of a chip in the post silicon era. The database is generated through an automated script which automates the retrieval of all the testing data associated with the chip cores and domains. In this work, scripting automation for generation of QSCAN database is developed to eliminate the manual maintenance which are prone to errors and are highly tedious. Automation scripts are developed using Perl language. Separate scripts are developed for stuck-at-faults and transition delay faults. The database is generated with configuration file containing the test patterns, its directories and other identities of the chip, marker files and validation files which has the files expressing the successful validation details of a chip. The database incorporates information on all the instances of a chip.
{"title":"Programming Routine Tasks Utilizing Scripting Automation in Generation of QSCAN Database","authors":"M.Thilagaraj, Kottaimalai Ramaraj, C.S.Sundar Ganesh, T.Vadivelan","doi":"10.1109/ICECAA58104.2023.10212198","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212198","url":null,"abstract":"This research study aims to generate a database in verification phase of a chip in the post silicon era. The database is generated through an automated script which automates the retrieval of all the testing data associated with the chip cores and domains. In this work, scripting automation for generation of QSCAN database is developed to eliminate the manual maintenance which are prone to errors and are highly tedious. Automation scripts are developed using Perl language. Separate scripts are developed for stuck-at-faults and transition delay faults. The database is generated with configuration file containing the test patterns, its directories and other identities of the chip, marker files and validation files which has the files expressing the successful validation details of a chip. The database incorporates information on all the instances of a chip.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"30 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":"127333414","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/ICECAA58104.2023.10212175
M. Eswaran, B. P, Pradeepa V
Human blastocyst is an embryo on its 5th day of development. The formation of 32 cell stage is called Blastocyst stage and its size is about 0.2mm. Blastocyst analysis is to automate blastocyst morphology by analyzing with multiple images. A fertilized egg is cultured for five days before being put into the uterus when using blastocysts in in-vitro fertilization. It might be a more successful fertility treatment alternative than standard in-vitro fertilization. The Blastocyst assessment aims to increase in-vitro fertilization success rates based on women age. Deep learning is an enabling technology to fulfill all of the above requirements and this model helps in assessing the morphology and cellular composition of blastocysts. Approximately 40% of human blastocysts are genetically normal, however this number drops to 25% if the woman was aged over 40 when her eggs were collected. The model performance is evaluated based on accuracy, loss, Precision and recall values. The Higher accuracy in blastocyst assessment can be achieved by training a DenseNet model on a large dataset of elucidated blastocyst images. This Model achieved a significantly higher accuracy of 92% by assessing the blastocyst development based on women age.
{"title":"Assessment of Human Blastocyst using Deep Learning Algorithm","authors":"M. Eswaran, B. P, Pradeepa V","doi":"10.1109/ICECAA58104.2023.10212175","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212175","url":null,"abstract":"Human blastocyst is an embryo on its 5th day of development. The formation of 32 cell stage is called Blastocyst stage and its size is about 0.2mm. Blastocyst analysis is to automate blastocyst morphology by analyzing with multiple images. A fertilized egg is cultured for five days before being put into the uterus when using blastocysts in in-vitro fertilization. It might be a more successful fertility treatment alternative than standard in-vitro fertilization. The Blastocyst assessment aims to increase in-vitro fertilization success rates based on women age. Deep learning is an enabling technology to fulfill all of the above requirements and this model helps in assessing the morphology and cellular composition of blastocysts. Approximately 40% of human blastocysts are genetically normal, however this number drops to 25% if the woman was aged over 40 when her eggs were collected. The model performance is evaluated based on accuracy, loss, Precision and recall values. The Higher accuracy in blastocyst assessment can be achieved by training a DenseNet model on a large dataset of elucidated blastocyst images. This Model achieved a significantly higher accuracy of 92% by assessing the blastocyst development based on women age.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"133 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":"116538138","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}
Social distancing and wearing a face mask correctly is known to be one of the most effective measures to fight against a pandemic like Covid 19. Thereupon no such precise system has been made and in this domain, research is still going on. In this study, mainly two deep learning models namely CNN, and YoloV5 are employed for object detection of face masks and social distancing and Vgg-19 for feature extraction. For the evaluation of the models, various parameters like precision, recall, mAP-mean average precision, accuracy, validation and training loss have been calculated. This has been observed that among all deployed deep learning models on the collected data, CNN (Convolutional Neural Network) outperformed with an accuracy of 99.3% and a precision of 98%.
{"title":"Face Mask Detection and Social Distancing using Deep Learning","authors":"Arunima Jaiswal, Khushboo Kem, Aruna Ippli, Lydia Nenghoithem Haokip, Nitin Sachdeva","doi":"10.1109/ICECAA58104.2023.10212278","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212278","url":null,"abstract":"Social distancing and wearing a face mask correctly is known to be one of the most effective measures to fight against a pandemic like Covid 19. Thereupon no such precise system has been made and in this domain, research is still going on. In this study, mainly two deep learning models namely CNN, and YoloV5 are employed for object detection of face masks and social distancing and Vgg-19 for feature extraction. For the evaluation of the models, various parameters like precision, recall, mAP-mean average precision, accuracy, validation and training loss have been calculated. This has been observed that among all deployed deep learning models on the collected data, CNN (Convolutional Neural Network) outperformed with an accuracy of 99.3% and a precision of 98%.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"147 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":"131556715","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/ICECAA58104.2023.10212425
D. Babu, Syed Mizbahuddin, Thouti Bharath Kumar, S. Supreeth, Goud Arukala, Naredla Phaneendra Reddy, A. .. S. Kumar
Plant diseases are mostly affecting leaves. In most of the cases, manual disease identification method fails to identify the disease correctly due to the similar symptoms of various diseases. People lack sufficient knowledge of plant diseases. The inability to detect the plant disease leads to crop production loss. Moreover, farmers have suffered significant losses as a result of a lack of sufficient understanding and direction to address the issue. This necessitates the need to develop a novel technology to detect the plant diseases. This study has attempted to develop an effective plant disease detection model using Convolutional Neural Networks (CNN). The proposed model has the ability to detect multiple diseases that occur in a single plant species. The results show the efficiency of the proposed model.
{"title":"Leaf Disease Detection using Machine Learning Algorithms","authors":"D. Babu, Syed Mizbahuddin, Thouti Bharath Kumar, S. Supreeth, Goud Arukala, Naredla Phaneendra Reddy, A. .. S. Kumar","doi":"10.1109/ICECAA58104.2023.10212425","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212425","url":null,"abstract":"Plant diseases are mostly affecting leaves. In most of the cases, manual disease identification method fails to identify the disease correctly due to the similar symptoms of various diseases. People lack sufficient knowledge of plant diseases. The inability to detect the plant disease leads to crop production loss. Moreover, farmers have suffered significant losses as a result of a lack of sufficient understanding and direction to address the issue. This necessitates the need to develop a novel technology to detect the plant diseases. This study has attempted to develop an effective plant disease detection model using Convolutional Neural Networks (CNN). The proposed model has the ability to detect multiple diseases that occur in a single plant species. The results show the efficiency of the proposed model.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"23 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":"132723555","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/ICECAA58104.2023.10212329
Gurpreet Singh, Subham Kumar Singh
Throughout the past couple of decades, machine learning (ML) has made its way into scientific research and engineering. Machine learning (ML) strategies are widely employed in processing information, data mining, especially scientific computation. Data visualization is essential. Despite the fact that numerous types of visualization tools are commonly used, the majority of them need sufficient coding knowledge, are developed for specific purposes, or are not free. Virtual reality (VR) provides intuitive interactivity and comprehensive visualization. Researchers use virtual reality to make it possible for any biomedical specialist to use a machine learning (DL) framework for picture analysis. Although ML models can be effective instruments for assessing information, they can additionally be difficult to comprehend and create. We have developed a ML development system based on virtual reality in order to render the technology more user-friendly and approachable. The intuitive interactivity and vivid visualisation are offered by virtual reality (VR). Any technical discipline can create a machine learning (ML) approach to recognising pictures using VR. This paper offers a thorough analysis of ML visualisation techniques, resources, and procedures. By looking at the visual analytical pipeline customers, and researchers place data visualisation into the visual analytics methodology. It present an analysis of the many chart types that are available for data visualisation and discuss guidelines for using each one while taking into account the unique circumstances of the given utilise case. There look more closely at a few of the latest and greatest exciting visualisation tools. We research visualisation challenges in each domain because each ML model is unique in terms to VR strategies. Finally, we present a summary of the main difficulties with ML visualisations.
{"title":"Analysis of Data Visualization Techniques Useful for Machine Learning and Visual Reality","authors":"Gurpreet Singh, Subham Kumar Singh","doi":"10.1109/ICECAA58104.2023.10212329","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212329","url":null,"abstract":"Throughout the past couple of decades, machine learning (ML) has made its way into scientific research and engineering. Machine learning (ML) strategies are widely employed in processing information, data mining, especially scientific computation. Data visualization is essential. Despite the fact that numerous types of visualization tools are commonly used, the majority of them need sufficient coding knowledge, are developed for specific purposes, or are not free. Virtual reality (VR) provides intuitive interactivity and comprehensive visualization. Researchers use virtual reality to make it possible for any biomedical specialist to use a machine learning (DL) framework for picture analysis. Although ML models can be effective instruments for assessing information, they can additionally be difficult to comprehend and create. We have developed a ML development system based on virtual reality in order to render the technology more user-friendly and approachable. The intuitive interactivity and vivid visualisation are offered by virtual reality (VR). Any technical discipline can create a machine learning (ML) approach to recognising pictures using VR. This paper offers a thorough analysis of ML visualisation techniques, resources, and procedures. By looking at the visual analytical pipeline customers, and researchers place data visualisation into the visual analytics methodology. It present an analysis of the many chart types that are available for data visualisation and discuss guidelines for using each one while taking into account the unique circumstances of the given utilise case. There look more closely at a few of the latest and greatest exciting visualisation tools. We research visualisation challenges in each domain because each ML model is unique in terms to VR strategies. Finally, we present a summary of the main difficulties with ML visualisations.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"77 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":"132219051","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/ICECAA58104.2023.10212358
K. K, O. G., Moulieswaran V, Miduna A, Mohammad Afnaan T M
In this research study, the “Magic Mirror”. “a voice-controlled wall mirror, is designed and implemented. It is a device that can simultaneously serve as a mirror and an interactive display, showing multimedia content such as time, date, and weather. Using voice commands, the user can communicate with the mirror. It is a device that can simultaneously serve as a mirror and an interactive display, showing multimedia content such as time, date and weather. The user can communicate with the mirror via voice commands. The Magic Mirror has a number of features, including voice commands via an LCD display and microphone, as well as real-time data and information updates. Users can communicate with the Magic Mirror via voice commands. The smart mirror is a mirror that can reflect light and display information, is a vibrant way to integrate two applications. The user can be recognized by Smart Mirror using the voice recognition model. To obtain current data to display on a Magic mirror, the Pi will connect to the internet.
{"title":"A Novel Two-Way Mirror with the Help of the Internet of Things","authors":"K. K, O. G., Moulieswaran V, Miduna A, Mohammad Afnaan T M","doi":"10.1109/ICECAA58104.2023.10212358","DOIUrl":"https://doi.org/10.1109/ICECAA58104.2023.10212358","url":null,"abstract":"In this research study, the “Magic Mirror”. “a voice-controlled wall mirror, is designed and implemented. It is a device that can simultaneously serve as a mirror and an interactive display, showing multimedia content such as time, date, and weather. Using voice commands, the user can communicate with the mirror. It is a device that can simultaneously serve as a mirror and an interactive display, showing multimedia content such as time, date and weather. The user can communicate with the mirror via voice commands. The Magic Mirror has a number of features, including voice commands via an LCD display and microphone, as well as real-time data and information updates. Users can communicate with the Magic Mirror via voice commands. The smart mirror is a mirror that can reflect light and display information, is a vibrant way to integrate two applications. The user can be recognized by Smart Mirror using the voice recognition model. To obtain current data to display on a Magic mirror, the Pi will connect to the internet.","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":"133170845","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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