Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141035
K. Manikandan, Kesamreddy Swapna, Narendra Naik J, K. N. Kumar, Kaku Rakesh, Kamisetty Vaishnavi
Economic dispatch for the microgrid (MG) is better adapted to the needs of a system in actual operation in the current scenario because it not only takes into account the scheduling cycle's lowest cost but also coordinates between several distributed generations (DGs) over a long period of time. Due of the unpredictable fluctuations and intervals that wind and solar energy are subject to, the economic dispatch problem is quite challenging to resolve. Intelligent algorithms and multi-objective optimum dispatching systems are acknowledged as excellent strategies for enhancing the economics and environmental friendliness of microgrid applications. The Multi Objective Optimal Dispatching System is developed for microgrids made up of photovoltaic cells (PV), wind turbines (WT), micro turbines (MT), fuel cells (FC), and battery storage (BT). The microgrid's dispatching problems might be solved and its dispatching convergence accuracy., stability., and speed all increased by using optimization techniques.
{"title":"Grey Wolf Optimization Algorithm based Combined Economic and Emission Dispatch Problem","authors":"K. Manikandan, Kesamreddy Swapna, Narendra Naik J, K. N. Kumar, Kaku Rakesh, Kamisetty Vaishnavi","doi":"10.1109/ICAAIC56838.2023.10141035","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141035","url":null,"abstract":"Economic dispatch for the microgrid (MG) is better adapted to the needs of a system in actual operation in the current scenario because it not only takes into account the scheduling cycle's lowest cost but also coordinates between several distributed generations (DGs) over a long period of time. Due of the unpredictable fluctuations and intervals that wind and solar energy are subject to, the economic dispatch problem is quite challenging to resolve. Intelligent algorithms and multi-objective optimum dispatching systems are acknowledged as excellent strategies for enhancing the economics and environmental friendliness of microgrid applications. The Multi Objective Optimal Dispatching System is developed for microgrids made up of photovoltaic cells (PV), wind turbines (WT), micro turbines (MT), fuel cells (FC), and battery storage (BT). The microgrid's dispatching problems might be solved and its dispatching convergence accuracy., stability., and speed all increased by using optimization techniques.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127411307","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140397
M. Kour, Neelam Sharma
Banking system across the globe is facing challenge due to cyber security threats. This has led financial institutions to rethink and redesign their business models. To get rid of cyber-attacks and security breach, intervention of technology is imperative. Stakeholders in the banking industry are quite worried about upsurge in the rate of cyber-crimes. Generally, cyber-attacks are done through software system running on a computer system in a cyber space. To safeguard software system against cyber-attacks it is utmost to detect entities operating within the cyber space and dangers to application security separated after examining the vulnerabilities and creating defense mechanism to reduce risks of cyber-attacks on software systems. Hence it is pertinent to understand security issues being faced by e-banking so that suitable measures can be taken accordingly. This paper is an attempt to understand different theories related to cyber security and also discusses various security threats to which e-banking is exposed.
{"title":"Security Issues in e-Banking","authors":"M. Kour, Neelam Sharma","doi":"10.1109/ICAAIC56838.2023.10140397","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140397","url":null,"abstract":"Banking system across the globe is facing challenge due to cyber security threats. This has led financial institutions to rethink and redesign their business models. To get rid of cyber-attacks and security breach, intervention of technology is imperative. Stakeholders in the banking industry are quite worried about upsurge in the rate of cyber-crimes. Generally, cyber-attacks are done through software system running on a computer system in a cyber space. To safeguard software system against cyber-attacks it is utmost to detect entities operating within the cyber space and dangers to application security separated after examining the vulnerabilities and creating defense mechanism to reduce risks of cyber-attacks on software systems. Hence it is pertinent to understand security issues being faced by e-banking so that suitable measures can be taken accordingly. This paper is an attempt to understand different theories related to cyber security and also discusses various security threats to which e-banking is exposed.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129113389","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140287
Dr RAMA DEVI ODUGU, Sai Krishna Pothini, Mulpuru Prasanna Kumari, Sowjanya. V, Uppalapati Naga Sai Charan
The goal of predicting subscriptions for OTT (Over-The-Top) platforms using machine learning is to devise a model which can accurately predict whether a customer will continue using this platform or not. This information is important for OTT companies to understand and optimize their marketing and retention efforts. Relevant data, such as customer demographics and viewing habits, is collected and analyzed to train the model. This process involves cleaning the data, selecting important features, and training a machine learningmodel. The model is then tested and validated using performance metrics. In short, this problem requires a comprehensive understanding of customer behavior and the use of machine learning to predict subscription decisions. The results can provide valuable insights for OTT companies to improve their customer understanding and retention efforts.
{"title":"Customer Churn Prediction using Machine Learning: Subcription Renewal on OTT Platforms","authors":"Dr RAMA DEVI ODUGU, Sai Krishna Pothini, Mulpuru Prasanna Kumari, Sowjanya. V, Uppalapati Naga Sai Charan","doi":"10.1109/ICAAIC56838.2023.10140287","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140287","url":null,"abstract":"The goal of predicting subscriptions for OTT (Over-The-Top) platforms using machine learning is to devise a model which can accurately predict whether a customer will continue using this platform or not. This information is important for OTT companies to understand and optimize their marketing and retention efforts. Relevant data, such as customer demographics and viewing habits, is collected and analyzed to train the model. This process involves cleaning the data, selecting important features, and training a machine learningmodel. The model is then tested and validated using performance metrics. In short, this problem requires a comprehensive understanding of customer behavior and the use of machine learning to predict subscription decisions. The results can provide valuable insights for OTT companies to improve their customer understanding and retention efforts.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122201174","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141488
Raj Kishor Bisht, Sarthak Sharma, Ashna Gusain, N. Thakur
Collocations are not merely frequently appearing word combinations (n-grams). Words in collocations have some kind of strong association among them. Collocations play an important role in various natural language processing (NLP) applications. Sentiment analysis is one of the growing areas of research in NLP because of its utilization in various business strategies. The present paper investigates collocations in positive and negative sentiments and their usefulness in sentiment analysis. We considered Amazon Products Review dataset for the purpose and analyzed positive and negative reviews separately. Different statistical techniques; Pointwise Mutual information (PMI), Chi Square test (Chi2), t-test, and likelihood ratio (LH) have been used to extract collocations from these texts and the common collocations have been extracted and analyzed. We found that collocation may be a potential feature for sentiment analysis.
{"title":"A Study of Collocations in Sentiment Analysis","authors":"Raj Kishor Bisht, Sarthak Sharma, Ashna Gusain, N. Thakur","doi":"10.1109/ICAAIC56838.2023.10141488","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141488","url":null,"abstract":"Collocations are not merely frequently appearing word combinations (n-grams). Words in collocations have some kind of strong association among them. Collocations play an important role in various natural language processing (NLP) applications. Sentiment analysis is one of the growing areas of research in NLP because of its utilization in various business strategies. The present paper investigates collocations in positive and negative sentiments and their usefulness in sentiment analysis. We considered Amazon Products Review dataset for the purpose and analyzed positive and negative reviews separately. Different statistical techniques; Pointwise Mutual information (PMI), Chi Square test (Chi2), t-test, and likelihood ratio (LH) have been used to extract collocations from these texts and the common collocations have been extracted and analyzed. We found that collocation may be a potential feature for sentiment analysis.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117023024","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140848
J. G. J. S. Raja, S. Juliet
In language processing, sentiment analysis is an essential task that involves analyzing and understanding the opinions, feelings, and emotions expressed in a text by users. In other words, it is a way of analyzing and understanding people's feelings. Since a large amount of data is generated by customers on a variety of online platforms, it has become increasingly important for businesses to analyze this data to better understand their customers' opinions and improve their products and services according to these opinions. One of the most well-known venues for opinion sharing is TripAdvisor, where customers discuss their experiences and reviews of hotels. This proposed work offers a method for the analysis of hotel reviews on TripAdvisor based on sentiment analysis using a deep learning-based approach. The study employs Bidirectional Encoder Representations from Transformers to classify the reviews by their sentiments, after learning the characteristics of the text data. Experimental results demonstrate the comparison of a few deep learning models and provide recommendation of the suitable model for customer feedback analysis. Hotels can utilize the suggested method to examine visitor comments.
{"title":"Deep Learning-based Sentiment Analysis of Trip Advisor Reviews","authors":"J. G. J. S. Raja, S. Juliet","doi":"10.1109/ICAAIC56838.2023.10140848","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140848","url":null,"abstract":"In language processing, sentiment analysis is an essential task that involves analyzing and understanding the opinions, feelings, and emotions expressed in a text by users. In other words, it is a way of analyzing and understanding people's feelings. Since a large amount of data is generated by customers on a variety of online platforms, it has become increasingly important for businesses to analyze this data to better understand their customers' opinions and improve their products and services according to these opinions. One of the most well-known venues for opinion sharing is TripAdvisor, where customers discuss their experiences and reviews of hotels. This proposed work offers a method for the analysis of hotel reviews on TripAdvisor based on sentiment analysis using a deep learning-based approach. The study employs Bidirectional Encoder Representations from Transformers to classify the reviews by their sentiments, after learning the characteristics of the text data. Experimental results demonstrate the comparison of a few deep learning models and provide recommendation of the suitable model for customer feedback analysis. Hotels can utilize the suggested method to examine visitor comments.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131286502","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141005
Rajasekhar Thiruthuvaraj, Ashly Ann Jo, Ebin Deni Raj
Recently, many companies are relying on Natural Language Processing (NLP) techniques to understand the text data generated daily. It has become very critical to deal with this data because finding the sentiments of text and summarizing them will help the company understand the pain points of the customers posting reviews on social media or understand the experience of the customer. These requirements have increasingly demanded many advanced algorithms to deal the text data. The introduction of Transformers led to businesses adopting NLP methods more and more to keep up with their needs. Models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), state-of-the-art results were achieved with billions of parameters learned. Although these advancements improved the accuracy and expanded the use of algorithms to a wide range of NLP tasks like language translation, text summarization, and language modeling. Businesses are more interested in the Explainability of the model compared to its accuracy. Explainable Artificial Intelligence (XAI) plays an important role to comprehend the complexities of the model as well as the influence of weights on predictions. In this paper, the complexities of the transformer model are unraveled by presenting a straightforward method for computing explainable predictions. The DistilBERT model is chosen as an example to implement the explainable system due to its lighter nature. Combining the strengths of a Posthoc expla-nation with those of a self-learning neural network, the method makes it simple to scale it to other algorithms to implement. With technologies like python, PyTorch, and Hugging Face, a detailed step-by-step algorithmic computation is demonstrated to explain the predictions from the attention-based explanations.
{"title":"Explainability to Business: Demystify Transformer Models with Attention-based Explanations","authors":"Rajasekhar Thiruthuvaraj, Ashly Ann Jo, Ebin Deni Raj","doi":"10.1109/ICAAIC56838.2023.10141005","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141005","url":null,"abstract":"Recently, many companies are relying on Natural Language Processing (NLP) techniques to understand the text data generated daily. It has become very critical to deal with this data because finding the sentiments of text and summarizing them will help the company understand the pain points of the customers posting reviews on social media or understand the experience of the customer. These requirements have increasingly demanded many advanced algorithms to deal the text data. The introduction of Transformers led to businesses adopting NLP methods more and more to keep up with their needs. Models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), state-of-the-art results were achieved with billions of parameters learned. Although these advancements improved the accuracy and expanded the use of algorithms to a wide range of NLP tasks like language translation, text summarization, and language modeling. Businesses are more interested in the Explainability of the model compared to its accuracy. Explainable Artificial Intelligence (XAI) plays an important role to comprehend the complexities of the model as well as the influence of weights on predictions. In this paper, the complexities of the transformer model are unraveled by presenting a straightforward method for computing explainable predictions. The DistilBERT model is chosen as an example to implement the explainable system due to its lighter nature. Combining the strengths of a Posthoc expla-nation with those of a self-learning neural network, the method makes it simple to scale it to other algorithms to implement. With technologies like python, PyTorch, and Hugging Face, a detailed step-by-step algorithmic computation is demonstrated to explain the predictions from the attention-based explanations.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132446537","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141156
Vanita G. Tonge, Asha Ambhaikar
Convolutional Neural Network (CNN) is a powerful tool used for classifying medical images. Based on extracted features from CT scan Image CNN classify it as malicious or non-malicious. Optimizers are strategies or methodologies which make a change in the weights of parameters in several iterations and try to minimize losses. Tuning hyperparameters of networks is time consuming and cumbersome task. For training a dataset many customized optimizers and metaheuristic algorithms are available. In this research study, the implementation and analysis of various customized optimizers are done on IQ-OTH/NCCD dataset. Out of six optimizers, Adam reaches 99.84% whereas RmsProp, Nadam and Admax occupied 1.
{"title":"Analysis of Customized Optimizers of Convolutional Neural Networks for Lung Cancer Detection","authors":"Vanita G. Tonge, Asha Ambhaikar","doi":"10.1109/ICAAIC56838.2023.10141156","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141156","url":null,"abstract":"Convolutional Neural Network (CNN) is a powerful tool used for classifying medical images. Based on extracted features from CT scan Image CNN classify it as malicious or non-malicious. Optimizers are strategies or methodologies which make a change in the weights of parameters in several iterations and try to minimize losses. Tuning hyperparameters of networks is time consuming and cumbersome task. For training a dataset many customized optimizers and metaheuristic algorithms are available. In this research study, the implementation and analysis of various customized optimizers are done on IQ-OTH/NCCD dataset. Out of six optimizers, Adam reaches 99.84% whereas RmsProp, Nadam and Admax occupied 1.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"134 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131745560","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140756
Nagagopiraju Vullam, S. Vellela, Venkateswara Reddy B, M. V. Rao, K. Sk, Roja D
As more sectors began to switch from conventional business models to e-commerce in response to the general trend toward mobile Internet use, the scale of e-commerce grew rapidly. There are three types of recommendation systems: hybrid, collaborative, content-based. Content based systems take into consideration the characteristics of the recommended objects. Then, titles in the database that have been classified as “romantic” are selected using a content-based recommendation method. Collaborative filtering systems utilize similarity measures to recommend items that are shared by individuals or objects with similar interests. Users are recommended items based on their preferences. In the recommendation system, collaborative filtering is the most popular and effective suggestion process. However, system performance impact as the amount of time required to locate the target user's closest neighbor across the entire user space increases with the number of users and products in the e-commerce system. The applied and designed Multi-Agent personalized recommendation system in E-commerce can be analyzed using user clustering in the Multi-Agent to E-commerce personalized recommendation system. An implementation strategy for recommendations based on user clustering is shown in this analysis. According to their scores for commodity categories, users are clustered, and only the nearest neighbours in their categories are searched, so that as many nearest neighbors as possible can be searched. The accuracy, recall, and specificity of this analysis are used to calculate its performance. In this analysis the presented method will give better results.
{"title":"Multi-Agent Personalized Recommendation System in E-Commerce based on User","authors":"Nagagopiraju Vullam, S. Vellela, Venkateswara Reddy B, M. V. Rao, K. Sk, Roja D","doi":"10.1109/ICAAIC56838.2023.10140756","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140756","url":null,"abstract":"As more sectors began to switch from conventional business models to e-commerce in response to the general trend toward mobile Internet use, the scale of e-commerce grew rapidly. There are three types of recommendation systems: hybrid, collaborative, content-based. Content based systems take into consideration the characteristics of the recommended objects. Then, titles in the database that have been classified as “romantic” are selected using a content-based recommendation method. Collaborative filtering systems utilize similarity measures to recommend items that are shared by individuals or objects with similar interests. Users are recommended items based on their preferences. In the recommendation system, collaborative filtering is the most popular and effective suggestion process. However, system performance impact as the amount of time required to locate the target user's closest neighbor across the entire user space increases with the number of users and products in the e-commerce system. The applied and designed Multi-Agent personalized recommendation system in E-commerce can be analyzed using user clustering in the Multi-Agent to E-commerce personalized recommendation system. An implementation strategy for recommendations based on user clustering is shown in this analysis. According to their scores for commodity categories, users are clustered, and only the nearest neighbours in their categories are searched, so that as many nearest neighbors as possible can be searched. The accuracy, recall, and specificity of this analysis are used to calculate its performance. In this analysis the presented method will give better results.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132100693","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140737
Keerti Kulkarni
Compressive Sensing is a relatively new technique for acquiring signals and images. This technique is a part of sparse signal processing and it exploits sparsity of the signal in one or the other domain. The main objective of this work is to show that sparse signal can be reconstructed with a lesser number of samples than that dictated by the Nyquist criteria. This research work considers a synthetically generated time domain sparse signal, and sample it using a random measurement matrix. Then, a time domain signal, which is sparse in the frequency domain is sampled using a delta matrix. This signal is first converted to the frequency domain using DFT. It is shown in this work that the reconstruction is better when 64 samples are used as compared to when 32 samples are used in the measurements.
{"title":"Analysis of the Measurement Matrices for Compressive Sensing of Signals","authors":"Keerti Kulkarni","doi":"10.1109/ICAAIC56838.2023.10140737","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140737","url":null,"abstract":"Compressive Sensing is a relatively new technique for acquiring signals and images. This technique is a part of sparse signal processing and it exploits sparsity of the signal in one or the other domain. The main objective of this work is to show that sparse signal can be reconstructed with a lesser number of samples than that dictated by the Nyquist criteria. This research work considers a synthetically generated time domain sparse signal, and sample it using a random measurement matrix. Then, a time domain signal, which is sparse in the frequency domain is sampled using a delta matrix. This signal is first converted to the frequency domain using DFT. It is shown in this work that the reconstruction is better when 64 samples are used as compared to when 32 samples are used in the measurements.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134435187","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 : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10140635
R. Arunadevi, S. Sudha, V. Karthi, M. D. Saranya, Thurai V B Raaj, Kavin Kumar K
Atherosclerosis is a chronic degenerative disease that results in cardiovascular diseases (CVDs) and is detected either by cardiac arrest or stroke. Early diagnosis of CVDs is made possible by identifying Intima Media Thickness (IMT) and elasticity. B-mode ultrasound imaging has on no account ionizing radiation and is economical and non-invasive to assess CVDs. This paper proposes an effective automatic image segmentation method using deep learning CNN for segmenting the region containing intima media of far wall carotid artery. The proposed approach is compared with SVM classifier and RBF neural network and is proven to be robust with improved accuracy and F1 score.
{"title":"Deep Learning based ROI Segmentation using Convolution Neural Network","authors":"R. Arunadevi, S. Sudha, V. Karthi, M. D. Saranya, Thurai V B Raaj, Kavin Kumar K","doi":"10.1109/ICAAIC56838.2023.10140635","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140635","url":null,"abstract":"Atherosclerosis is a chronic degenerative disease that results in cardiovascular diseases (CVDs) and is detected either by cardiac arrest or stroke. Early diagnosis of CVDs is made possible by identifying Intima Media Thickness (IMT) and elasticity. B-mode ultrasound imaging has on no account ionizing radiation and is economical and non-invasive to assess CVDs. This paper proposes an effective automatic image segmentation method using deep learning CNN for segmenting the region containing intima media of far wall carotid artery. The proposed approach is compared with SVM classifier and RBF neural network and is proven to be robust with improved accuracy and F1 score.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131753813","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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