Pub Date : 2023-05-04DOI: 10.1109/ICAAIC56838.2023.10141166
Koushik K S, Ankita Mahale, Shobha Rani N
One of the most important tasks in the realm of document analysis and recognition is the detection of equations in documents that were acquired using a camera. The procedure includes several steps, including pre-processing of the images, segmentation, feature extraction, and classification. The suggested method comprises taking a user-provided input expression image and classifying it into one of three types of equations: simple, complex, and highly complex. By choosing a decision boundary set off from the initial hyperplane, the SVR algorithm encodes the image, producing a model that fits the data better. The result is then obtained by character-wise segmenting the image and comparing it with trained models. Two recurrent neural networks make up the RNN encoder-decoder that is used. One RNN creates a fixed-length vector representation from a sequence of symbols, and a different RNN decodes that representation into a different sequence of symbols. 1900 images containing various equations made up the dataset utilized for training, validating, and testing the SVR and RNN. The accuracy of the system was about 93.64%.
{"title":"Equation Detection in the Camera Captured Handwritten Document","authors":"Koushik K S, Ankita Mahale, Shobha Rani N","doi":"10.1109/ICAAIC56838.2023.10141166","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141166","url":null,"abstract":"One of the most important tasks in the realm of document analysis and recognition is the detection of equations in documents that were acquired using a camera. The procedure includes several steps, including pre-processing of the images, segmentation, feature extraction, and classification. The suggested method comprises taking a user-provided input expression image and classifying it into one of three types of equations: simple, complex, and highly complex. By choosing a decision boundary set off from the initial hyperplane, the SVR algorithm encodes the image, producing a model that fits the data better. The result is then obtained by character-wise segmenting the image and comparing it with trained models. Two recurrent neural networks make up the RNN encoder-decoder that is used. One RNN creates a fixed-length vector representation from a sequence of symbols, and a different RNN decodes that representation into a different sequence of symbols. 1900 images containing various equations made up the dataset utilized for training, validating, and testing the SVR and RNN. The accuracy of the system was about 93.64%.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"405 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":"127597687","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/ICAAIC56838.2023.10140470
Arun Biradar, M. Chandan, Y. Raghavendra, K. Chidambarathanu, I. Thamarai, Anuj Raturi
The advent of software-defined networking and virtualization of network functions has brought numerous advantages; however, to achieve the flexibility and programmability envisaged in these technologies, new components in the control and management planes were introduced. Such components require fast recovery because without management the entire data plane is inoperable. To deal with flaws in these plans, the self-healing technique is used, explored in the work that is summarized in this document. The results prove the self-healing efficiency in network slices with strict quality requirements and also demonstrate that the introduced framework is capable of self-healing, that is, healing the degraded environment as well as healing itself.
{"title":"Self-Healing for Software Defined Networking","authors":"Arun Biradar, M. Chandan, Y. Raghavendra, K. Chidambarathanu, I. Thamarai, Anuj Raturi","doi":"10.1109/ICAAIC56838.2023.10140470","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140470","url":null,"abstract":"The advent of software-defined networking and virtualization of network functions has brought numerous advantages; however, to achieve the flexibility and programmability envisaged in these technologies, new components in the control and management planes were introduced. Such components require fast recovery because without management the entire data plane is inoperable. To deal with flaws in these plans, the self-healing technique is used, explored in the work that is summarized in this document. The results prove the self-healing efficiency in network slices with strict quality requirements and also demonstrate that the introduced framework is capable of self-healing, that is, healing the degraded environment as well as healing itself.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"121 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":"132702953","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/ICAAIC56838.2023.10141462
A. Lakshmanarao, M.Naveen Kumar, K.S.V. Ratnakar, Y. Satwika
India's economy is heavily dependent on agriculture, and this study report tries to increase agricultural productivity by forecasting crop yields for a range of crops farmed there. This study is unique in that it forecasts agricultural yields for any chosen time period throughout the year by using simple factors like, district, area, season and State. The article forecasts agricultural production using modern regression techniques including Lasso, Kernel Ridge, and Elastic-Net Regression designs. The idea of Stacking Regression is also used to improve the performance of the designs and provide more accurate forecasts. This research provides a positive breakthrough for India's agricultural industry, with the potential to deliver major advantages for farmers and the larger economy. This study provides a useful tool for improving crop yield projections and eventually increasing agricultural output in the nation by employing cutting-edge analytical methodologies and simple input parameters. Informed decisions regarding crop cultivation, fertilization, and harvest may be made by farmers with the help of technology and data-driven insights, resulting in higher yields and more favorable economic consequences.
{"title":"Crop Yield Prediction using Regression Models in Machine Learning","authors":"A. Lakshmanarao, M.Naveen Kumar, K.S.V. Ratnakar, Y. Satwika","doi":"10.1109/ICAAIC56838.2023.10141462","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141462","url":null,"abstract":"India's economy is heavily dependent on agriculture, and this study report tries to increase agricultural productivity by forecasting crop yields for a range of crops farmed there. This study is unique in that it forecasts agricultural yields for any chosen time period throughout the year by using simple factors like, district, area, season and State. The article forecasts agricultural production using modern regression techniques including Lasso, Kernel Ridge, and Elastic-Net Regression designs. The idea of Stacking Regression is also used to improve the performance of the designs and provide more accurate forecasts. This research provides a positive breakthrough for India's agricultural industry, with the potential to deliver major advantages for farmers and the larger economy. This study provides a useful tool for improving crop yield projections and eventually increasing agricultural output in the nation by employing cutting-edge analytical methodologies and simple input parameters. Informed decisions regarding crop cultivation, fertilization, and harvest may be made by farmers with the help of technology and data-driven insights, resulting in higher yields and more favorable economic consequences.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"56 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133086663","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/ICAAIC56838.2023.10141450
B. Rakesh, D. Ragavi, M. K. Reddy, G. L. Sumalata
Microvascular leakage within the retina causes the illness known as diabetic retinopathy (DR) in the eye. For people with diabetes mellitus (DM), diabetic retinopathy is the main reason for vision loss. This Disease is a global health issue, as the condition can lead to long-term disability and decreased quality of life for affected individuals. As a result, It causes microvascular issues and irreversible vision loss due to increase in sugar levels. Unfortunately, the accuracy of existing approaches is limited because of issues such as inadequate contrast, imaging quality, and lesion unpredictability. We propose a VGG-19 convolutional neural network technique for the identification and classification of NPDR in this research. Overcoming these obstacles, our goal is to design a system that can detect and classify NPDR from retinal pictures. Our findings show that our proposed technique is effective in reaching high accuracy and might potentially contribute to the early identification and treatment of NPDR. We also created a user interface for classification and detection of the severity of the disease.
{"title":"Detection and Classification of Non-Proliferation Diabetic Retinopathy using VGG-19 CNN Algorithm","authors":"B. Rakesh, D. Ragavi, M. K. Reddy, G. L. Sumalata","doi":"10.1109/ICAAIC56838.2023.10141450","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10141450","url":null,"abstract":"Microvascular leakage within the retina causes the illness known as diabetic retinopathy (DR) in the eye. For people with diabetes mellitus (DM), diabetic retinopathy is the main reason for vision loss. This Disease is a global health issue, as the condition can lead to long-term disability and decreased quality of life for affected individuals. As a result, It causes microvascular issues and irreversible vision loss due to increase in sugar levels. Unfortunately, the accuracy of existing approaches is limited because of issues such as inadequate contrast, imaging quality, and lesion unpredictability. We propose a VGG-19 convolutional neural network technique for the identification and classification of NPDR in this research. Overcoming these obstacles, our goal is to design a system that can detect and classify NPDR from retinal pictures. Our findings show that our proposed technique is effective in reaching high accuracy and might potentially contribute to the early identification and treatment of NPDR. We also created a user interface for classification and detection of the severity of the disease.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"124 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":"133599688","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/ICAAIC56838.2023.10140561
Dileep Kumar Boyapati, Jagathi Gottipati, Vinod Kattula, S. Yelisetti
Sentiment analysis, commonly referred to as opinion mining, reveals the attitudes and feelings of consumers about specific goods or services. The sentiment polarity classification, which identifies whether a review is favourable, negative, or neutral, is the fundamental issue with sentiment analysis. There are still some study gaps, as some studies only investigate the positive, neutral, and negative sentiment classes; none of these studies considered more than three classes; also, none of these studies considered the individual and combined effects of the sentiment polarity aspects. No prior method took into account the verb, adverb, adjective, and their combinations, as well as the five sentiment classes and three sentiment polarity traits. This study, provides a method for categorizing online reviews of Instant Videos based on their sentiment. Proposed study makes use of a substantial data set of 500,000 internet reviews. This review-level categorization process Adjective, verb, and two polarity traits are taken into account additionally as well as their pairings with various senses.
{"title":"Sentiment Polarity Categorization of Product Reviews using Twitter Data","authors":"Dileep Kumar Boyapati, Jagathi Gottipati, Vinod Kattula, S. Yelisetti","doi":"10.1109/ICAAIC56838.2023.10140561","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140561","url":null,"abstract":"Sentiment analysis, commonly referred to as opinion mining, reveals the attitudes and feelings of consumers about specific goods or services. The sentiment polarity classification, which identifies whether a review is favourable, negative, or neutral, is the fundamental issue with sentiment analysis. There are still some study gaps, as some studies only investigate the positive, neutral, and negative sentiment classes; none of these studies considered more than three classes; also, none of these studies considered the individual and combined effects of the sentiment polarity aspects. No prior method took into account the verb, adverb, adjective, and their combinations, as well as the five sentiment classes and three sentiment polarity traits. This study, provides a method for categorizing online reviews of Instant Videos based on their sentiment. Proposed study makes use of a substantial data set of 500,000 internet reviews. This review-level categorization process Adjective, verb, and two polarity traits are taken into account additionally as well as their pairings with various senses.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"114 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":"131841828","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/ICAAIC56838.2023.10140622
Raksha Pandey, A. Kushwaha, Suraj Sharma, Ankit Anand, Suraj Kumar
With the increase in sharing of videos worldwide over social networks, presence of high-quality fakes is on increase. Forged videos affect the authenticity and integrity of the video as a whole. This can lead to serious implications. For example, in case of video to be used in courts as an evidence, presence of forgery can implicate innocents or help criminal to escape justice. This calls for the detection mechanisms to counter. This leads to the discovery of several different approaches to detect copy-move forgery by analysing the side effects due to tempering. One of the most common approaches is copy-move video forgery which consists of duplicating area of frame. Traditional approach detects for patterns related to duplication manually which is not so successful. In contrast, methods related to deep learning gives better results. Therefore, this research follows deep learning model using pertained architecture to detect copy-move video forgery.
{"title":"Intra-frame Copy-move Video Forgery Detection","authors":"Raksha Pandey, A. Kushwaha, Suraj Sharma, Ankit Anand, Suraj Kumar","doi":"10.1109/ICAAIC56838.2023.10140622","DOIUrl":"https://doi.org/10.1109/ICAAIC56838.2023.10140622","url":null,"abstract":"With the increase in sharing of videos worldwide over social networks, presence of high-quality fakes is on increase. Forged videos affect the authenticity and integrity of the video as a whole. This can lead to serious implications. For example, in case of video to be used in courts as an evidence, presence of forgery can implicate innocents or help criminal to escape justice. This calls for the detection mechanisms to counter. This leads to the discovery of several different approaches to detect copy-move forgery by analysing the side effects due to tempering. One of the most common approaches is copy-move video forgery which consists of duplicating area of frame. Traditional approach detects for patterns related to duplication manually which is not so successful. In contrast, methods related to deep learning gives better results. Therefore, this research follows deep learning model using pertained architecture to detect copy-move video forgery.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"29 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":"133843011","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/ICAAIC56838.2023.10141005
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/ICAAIC56838.2023.10140848
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/ICAAIC56838.2023.10140397
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}
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