Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037341
H. Ingale, Shekhar S. Suralkar, Anil J. Patil
Most of recent events have attracted a lot of attention towards importance of automatic crowd classification and management. COVID-19 is the most setback for the entire world. During these events proper breakout and public crowd management leads to the requirement of managing, counting, securing as well as tracking the crowd. But automatic analysis of the crowd is very challenging task because of varying climatic and lighting conditions, varying postures etc. During this paper we have developed PYTHON based system for automatic crowd images classification using Deep learning. This paper is the first attempt for automatic classification of crowd images. We have prepared the dataset of crowd classification consisting of three categories. The proposed methodology of crowd classification starts with preprocessing during which we have used median filtering for noise removal. Deep learning models are developed using 70% training images. The performance of the system is evaluated for various deep learning algorithms including one block VGG, two block VGG and three block VGG. We have also evaluated the performance of three block VGG using dropout. VGG16 transfer learning based crowd classification is developed using PYTHON. Using VGG16 transfer learning we achieved the accuracy of 69.44.% which is highest among all deep learning classification models during this study
{"title":"Deep Learning for Crowd Image Classification for Images Captured Under Varying Climatic and Lighting Condition","authors":"H. Ingale, Shekhar S. Suralkar, Anil J. Patil","doi":"10.1109/IBSSC56953.2022.10037341","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037341","url":null,"abstract":"Most of recent events have attracted a lot of attention towards importance of automatic crowd classification and management. COVID-19 is the most setback for the entire world. During these events proper breakout and public crowd management leads to the requirement of managing, counting, securing as well as tracking the crowd. But automatic analysis of the crowd is very challenging task because of varying climatic and lighting conditions, varying postures etc. During this paper we have developed PYTHON based system for automatic crowd images classification using Deep learning. This paper is the first attempt for automatic classification of crowd images. We have prepared the dataset of crowd classification consisting of three categories. The proposed methodology of crowd classification starts with preprocessing during which we have used median filtering for noise removal. Deep learning models are developed using 70% training images. The performance of the system is evaluated for various deep learning algorithms including one block VGG, two block VGG and three block VGG. We have also evaluated the performance of three block VGG using dropout. VGG16 transfer learning based crowd classification is developed using PYTHON. Using VGG16 transfer learning we achieved the accuracy of 69.44.% which is highest among all deep learning classification models during this study","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124378578","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037492
Md Jubaer Hossain, M. Bhuiyan, Z. Abdullah
Detecting potential locations of CpG islands is one of the first steps for predicting promoter regions of many housekeeping and tissue-specific genes, which in turn, helps identify many epigenetic causes of cancer. Traditionally, finding potential CpG islands computationally involves calculating many manual-features and making different assumptions. Recently, in Natural Language Processing(NLP), transformer architectures incorporating mulit-head attention have surpassed many other sequence processing architectures such as RNN, GRU, LSTM etc. in terms of accuracy, speed, and computational efficiency. One of the major attributes of NLP is Named Entity Recognition(NER), which extracts the relevant information from a long sequence. In this study, CpG island identification is considered as an NER problem and transformer architecture is used for its detection. Conditional random field is further incorporated to include the dependencies of the associated labels. Additional attention mask is included on the input layer to give more importance to the regions relevant to DNA sequence. The publicly available EMBL human DNA database is used for experiments. It is observed that more than 96 % accuracy and 73 % F1-score can be achieved, a superior performance as compared to the existing results in the literature. The proposed approach can be utilized for identifying bio-markers for different important and disease-related genes efficiently. In addition, it may be used for other genome sequence analysis and processing tasks.
{"title":"CpG Island Detection Using Transformer Model with Conditional Random Field","authors":"Md Jubaer Hossain, M. Bhuiyan, Z. Abdullah","doi":"10.1109/IBSSC56953.2022.10037492","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037492","url":null,"abstract":"Detecting potential locations of CpG islands is one of the first steps for predicting promoter regions of many housekeeping and tissue-specific genes, which in turn, helps identify many epigenetic causes of cancer. Traditionally, finding potential CpG islands computationally involves calculating many manual-features and making different assumptions. Recently, in Natural Language Processing(NLP), transformer architectures incorporating mulit-head attention have surpassed many other sequence processing architectures such as RNN, GRU, LSTM etc. in terms of accuracy, speed, and computational efficiency. One of the major attributes of NLP is Named Entity Recognition(NER), which extracts the relevant information from a long sequence. In this study, CpG island identification is considered as an NER problem and transformer architecture is used for its detection. Conditional random field is further incorporated to include the dependencies of the associated labels. Additional attention mask is included on the input layer to give more importance to the regions relevant to DNA sequence. The publicly available EMBL human DNA database is used for experiments. It is observed that more than 96 % accuracy and 73 % F1-score can be achieved, a superior performance as compared to the existing results in the literature. The proposed approach can be utilized for identifying bio-markers for different important and disease-related genes efficiently. In addition, it may be used for other genome sequence analysis and processing tasks.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129795415","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}
Data mining is a broad field that helps the company to combine statistics, databases, machine learning, and artificial intelligence. As the size of the company grows, so do such situations, making it impossible for a normal information system to manage such perilous scenarios. Due to this companies face significant income loss since customers are leaving the firm for unexplained reasons. It is well acknowledged that acquiring new clients is more cash intensive than maintaining existing ones and hence customer management is critically impacted by customer churn, which happens when a customer decides he no longer wants to keep in touch with the company. Traditional market research methodologies are challenging to support the churn problem. There is still much potential for improvement in churn forecast accuracy despite the development of several churn prediction tools that look at hundreds of parameters. Ultimately, this research will aid in the analysis of consumer behavior and the categorization of whether or not a client is churning through the use of a variety of data mining approaches to predict customer churn. Using a data set available on Kaggle's website, this study tested multiple classifiers on the problem of predicting customers' propensity to leave a company. In this study, we utilized Kaggle's online data set to predict customer churn behavior using several classifiers, including Random Forest, Logistic, J48, Stacking, ADA Boost, Decision Table, and Logit Boost, and observed that our model achieved 93.55 percent accuracy.
{"title":"Forecasting Customer Churn in the Telecommunications Industry","authors":"Kritarth Gupta, Atharva Hardikar, Devansh Gupta, Shweta Loonkar","doi":"10.1109/IBSSC56953.2022.10037334","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037334","url":null,"abstract":"Data mining is a broad field that helps the company to combine statistics, databases, machine learning, and artificial intelligence. As the size of the company grows, so do such situations, making it impossible for a normal information system to manage such perilous scenarios. Due to this companies face significant income loss since customers are leaving the firm for unexplained reasons. It is well acknowledged that acquiring new clients is more cash intensive than maintaining existing ones and hence customer management is critically impacted by customer churn, which happens when a customer decides he no longer wants to keep in touch with the company. Traditional market research methodologies are challenging to support the churn problem. There is still much potential for improvement in churn forecast accuracy despite the development of several churn prediction tools that look at hundreds of parameters. Ultimately, this research will aid in the analysis of consumer behavior and the categorization of whether or not a client is churning through the use of a variety of data mining approaches to predict customer churn. Using a data set available on Kaggle's website, this study tested multiple classifiers on the problem of predicting customers' propensity to leave a company. In this study, we utilized Kaggle's online data set to predict customer churn behavior using several classifiers, including Random Forest, Logistic, J48, Stacking, ADA Boost, Decision Table, and Logit Boost, and observed that our model achieved 93.55 percent accuracy.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121185779","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037523
Inderpreet Singh
Hotel room pricing is a very common use case in the hospitality industry. Such use cases take dynamic pricing strategies for setting optimum prices wherein prices are dynamically adjusted based on user engagement. However, it is challenging to design an approach that makes pricing dynamic with respect to complex market change. In this paper, we suggest a reinforcement learning based solution for this problem. The approach employs a Deep Q-Network (DQN) agent trained to recommend/suggest optimum pricing strategies which maximizes the total profits for a day. In addition, the pricing strategy is optimized in such a way that empty rooms remain minimal. A real-life hotel-bookings data set is being used for testing this approach. The data is aggregated and preprocessed before being used for the task. The pricing strategy is influenced by the hotel-demand, type of rooms, number of nights and other variables. The hotel-demand is derived from a Random-forest model trained on the processed data to simulate original demand distribution of processed data. Using the DQN based dynamic pricing strategy, a potential 15–20 percentage higher reward(profits) were obtained compared to fixed pricing, and rule-based pricing strategy. At the same time the empty rooms left were significantly lower for the DQN based approach.
{"title":"Dynamic Pricing using Reinforcement Learning in Hospitality Industry","authors":"Inderpreet Singh","doi":"10.1109/IBSSC56953.2022.10037523","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037523","url":null,"abstract":"Hotel room pricing is a very common use case in the hospitality industry. Such use cases take dynamic pricing strategies for setting optimum prices wherein prices are dynamically adjusted based on user engagement. However, it is challenging to design an approach that makes pricing dynamic with respect to complex market change. In this paper, we suggest a reinforcement learning based solution for this problem. The approach employs a Deep Q-Network (DQN) agent trained to recommend/suggest optimum pricing strategies which maximizes the total profits for a day. In addition, the pricing strategy is optimized in such a way that empty rooms remain minimal. A real-life hotel-bookings data set is being used for testing this approach. The data is aggregated and preprocessed before being used for the task. The pricing strategy is influenced by the hotel-demand, type of rooms, number of nights and other variables. The hotel-demand is derived from a Random-forest model trained on the processed data to simulate original demand distribution of processed data. Using the DQN based dynamic pricing strategy, a potential 15–20 percentage higher reward(profits) were obtained compared to fixed pricing, and rule-based pricing strategy. At the same time the empty rooms left were significantly lower for the DQN based approach.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131426711","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037368
Pranoti Nage, Amey Pandit, Shravani Jeurkar, S. Shitole
Diabetes is a chronic health condition that arises due to inability to maintain a healthy glucose level in the blood. Over a period of time, due to this condition body organ of any individual may get damaged as diabetes affects primary organs like heart, blood vessels, eyes, brain, etc. The main cause of Diabetic Retinopathy is diabetes mellitus, which causes vision problems due to excess swelling of blood vessels of the retina, which further causes leakage of fluids and blood, into retina membrane. Almost 60% to 80% of diabetes patients who are suffering from chronic diabetes suffer from diabetic retinopathy. It is a leading factor for blindness in people from age 21 to 60 years. Diabetic Retinopathy can be treated in the early stage by observing abnormal growth of tissues called lesions which start from Micro-aneurysms in the non-proliferative stage of Diabetic Retinopathy. Many researchers throughout the world have proposed numerous Machine Learning models for early detection of Diabetic Retinopathy from developing into later stages, that is, to prevent blindness. In this paper, android application is developed to detect severity of Diabetic Retinopathy using deep learning techniques.
{"title":"Diabetic Retinopathy Detection using Android Application","authors":"Pranoti Nage, Amey Pandit, Shravani Jeurkar, S. Shitole","doi":"10.1109/IBSSC56953.2022.10037368","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037368","url":null,"abstract":"Diabetes is a chronic health condition that arises due to inability to maintain a healthy glucose level in the blood. Over a period of time, due to this condition body organ of any individual may get damaged as diabetes affects primary organs like heart, blood vessels, eyes, brain, etc. The main cause of Diabetic Retinopathy is diabetes mellitus, which causes vision problems due to excess swelling of blood vessels of the retina, which further causes leakage of fluids and blood, into retina membrane. Almost 60% to 80% of diabetes patients who are suffering from chronic diabetes suffer from diabetic retinopathy. It is a leading factor for blindness in people from age 21 to 60 years. Diabetic Retinopathy can be treated in the early stage by observing abnormal growth of tissues called lesions which start from Micro-aneurysms in the non-proliferative stage of Diabetic Retinopathy. Many researchers throughout the world have proposed numerous Machine Learning models for early detection of Diabetic Retinopathy from developing into later stages, that is, to prevent blindness. In this paper, android application is developed to detect severity of Diabetic Retinopathy using deep learning techniques.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132376997","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037486
R. Patil, Prasad Peshave, Milind Kamble
Doctor's handwritten prescriptions are often known to be indecipherable. Uncertainty in medical terms can have dire consequences. A method to effectively recognize medicine names written in doctor's handwriting is proposed in this paper. A corpus of 600 images is compiled with the help of multiple doctors. An exhaustive list of 50 medicines is used for the same. Recognition is performed using the Convolutional Recurrent Neural Network (CRNN) - Connectionist Temporal Classification (CTC) model which results in 93.3 % accuracy. In order to deal with errors produced in the recognized text, edit distance methods are further implemented and analyzed. Damerau-Levenshtein distance method is deemed to be the most suitable, yielding a well-grounded system for medicine name recognition.
{"title":"Application of Fuzzy Matching Algorithms for Doctors Handwriting Recognition","authors":"R. Patil, Prasad Peshave, Milind Kamble","doi":"10.1109/IBSSC56953.2022.10037486","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037486","url":null,"abstract":"Doctor's handwritten prescriptions are often known to be indecipherable. Uncertainty in medical terms can have dire consequences. A method to effectively recognize medicine names written in doctor's handwriting is proposed in this paper. A corpus of 600 images is compiled with the help of multiple doctors. An exhaustive list of 50 medicines is used for the same. Recognition is performed using the Convolutional Recurrent Neural Network (CRNN) - Connectionist Temporal Classification (CTC) model which results in 93.3 % accuracy. In order to deal with errors produced in the recognized text, edit distance methods are further implemented and analyzed. Damerau-Levenshtein distance method is deemed to be the most suitable, yielding a well-grounded system for medicine name recognition.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133099396","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037284
Ankit Basrur, Dhrumil Mehta, Abhijit Joshi
This paper proposes the application of Transfer Learning in classifying a food dish. Traditional methods involve using Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), which are highly inefficient when the classes in a dataset increase. Therefore, more modern ways of classification become vital to adapt to evolving human tastes. Thus, we have achieved excellent results by leveraging Neural Networks in the form of ResNet, VGG19, EfficientNet, and DenseNet. Additionally, a web crawler has been integrated to provide the recipe for the same dish.
{"title":"Food Recognition using Transfer Learning","authors":"Ankit Basrur, Dhrumil Mehta, Abhijit Joshi","doi":"10.1109/IBSSC56953.2022.10037284","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037284","url":null,"abstract":"This paper proposes the application of Transfer Learning in classifying a food dish. Traditional methods involve using Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), which are highly inefficient when the classes in a dataset increase. Therefore, more modern ways of classification become vital to adapt to evolving human tastes. Thus, we have achieved excellent results by leveraging Neural Networks in the form of ResNet, VGG19, EfficientNet, and DenseNet. Additionally, a web crawler has been integrated to provide the recipe for the same dish.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115554049","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 : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037427
Pranav More, Sushila Ratre, Sunil Ligade, Rajesh H. Bhise
In every country on this planet, COVID-19 disease s right now one of the most unsafe issues. The expedient and precise space of the Covid virus infection s major to see and take better treatment for the infected patients will increase the chance of saving their lives. The quick spread of the Covid virus has blended complete interest and caused greater than 10 lacks cases to date. To battle this spread, Chest CTs arise as a basic demonstrative contraption for the clinical association of COVID-19 related to a lung illness. A modified confirmation device is essential for assisting in the screening for COVID-19 pneumonia by making use of chest CT imaging. The COVID-19 illness detection utilizing supplementary GoogLeNet is shown in this study. Deep Convolutional Neural Networks were built by researchers at Google, and one of their innovations was the Inception Network. GoogLeNet is a 22-layer deep convolutional neural network that is a variation of the inception Network. GoogLeNet is utilized for a variety of additional computer vision applications nowadays, including face identification and recognition, adversarial training, and so on. The findings indicate that the GoogLeNet method is superior to the CNN Method in terms of its ability to detect COVID-19 sickness.
{"title":"Design of an Efficient Approach for Performance Enhancement of COVID-19 Detection Using Auxiliary GoogLeNet by Using Chest CT Scan Images","authors":"Pranav More, Sushila Ratre, Sunil Ligade, Rajesh H. Bhise","doi":"10.1109/IBSSC56953.2022.10037427","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037427","url":null,"abstract":"In every country on this planet, COVID-19 disease s right now one of the most unsafe issues. The expedient and precise space of the Covid virus infection s major to see and take better treatment for the infected patients will increase the chance of saving their lives. The quick spread of the Covid virus has blended complete interest and caused greater than 10 lacks cases to date. To battle this spread, Chest CTs arise as a basic demonstrative contraption for the clinical association of COVID-19 related to a lung illness. A modified confirmation device is essential for assisting in the screening for COVID-19 pneumonia by making use of chest CT imaging. The COVID-19 illness detection utilizing supplementary GoogLeNet is shown in this study. Deep Convolutional Neural Networks were built by researchers at Google, and one of their innovations was the Inception Network. GoogLeNet is a 22-layer deep convolutional neural network that is a variation of the inception Network. GoogLeNet is utilized for a variety of additional computer vision applications nowadays, including face identification and recognition, adversarial training, and so on. The findings indicate that the GoogLeNet method is superior to the CNN Method in terms of its ability to detect COVID-19 sickness.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115682942","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}
Natural Language Processing is a subset of Artificial Intelligence, which focuses more on the natural language communication and speech recognition. After evaluation of AI and its sub branches, the automated answering system or now it's called as chatbot is a very popular and widely used application. One of the limitations of this application is that it is text based. This application is not so effective when we want to develop a dynamic system. In this paper, authors have proposed a VideoBot application, which is a more effective way of communication while interacting with end users. While simple chatbots, which don't have any emotional attachment with end users, as compared to this Videobot have more effectively connected with end-users, as it has videos with emotional expressions.
{"title":"Advancement in Communication using Natural Language based VideoBot System","authors":"Flewin Dsouza, Rushikesh Shaharao, Yashsingh Thakur, Pranav Agwan, Gopal Sakarkar, Piyush Gupta","doi":"10.1109/IBSSC56953.2022.10037380","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037380","url":null,"abstract":"Natural Language Processing is a subset of Artificial Intelligence, which focuses more on the natural language communication and speech recognition. After evaluation of AI and its sub branches, the automated answering system or now it's called as chatbot is a very popular and widely used application. One of the limitations of this application is that it is text based. This application is not so effective when we want to develop a dynamic system. In this paper, authors have proposed a VideoBot application, which is a more effective way of communication while interacting with end users. While simple chatbots, which don't have any emotional attachment with end users, as compared to this Videobot have more effectively connected with end-users, as it has videos with emotional expressions.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115900178","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}
The internet and web applications are the only things that run the modern world. Today, the biggest concern facing businesses is web security. It is seen as serving as the fundamental framework for the global data society. Security breaches can happen to web applications. Web security is merely protecting a layer of a web application from attacks by attackers or unauthorized users. A large number of problems with web based applications are mostly the result of incorrect client input. The various facets of web security are covered in this paper, along with its flaws. This paper also discusses the key components of web security strategies, including encryption, authentication, passwords, and integrity. Additionally described in detail are the attack methods and the anatomy of a web based application attack. This paper explores a number of methods for detection and prevention of vulnerabilities in the web application. This research suggests a more effective method for reducing this category of web vulnerabilities. Additionally, it offers the finest defence against the a for mentioned threats.
{"title":"Cyber Attack Detection and Implementation of Prevention Methods For Web Application","authors":"Aishwarya Bhalme, Akash Pawar, Aditi Borkar, Pranav Shriram","doi":"10.1109/IBSSC56953.2022.10037431","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037431","url":null,"abstract":"The internet and web applications are the only things that run the modern world. Today, the biggest concern facing businesses is web security. It is seen as serving as the fundamental framework for the global data society. Security breaches can happen to web applications. Web security is merely protecting a layer of a web application from attacks by attackers or unauthorized users. A large number of problems with web based applications are mostly the result of incorrect client input. The various facets of web security are covered in this paper, along with its flaws. This paper also discusses the key components of web security strategies, including encryption, authentication, passwords, and integrity. Additionally described in detail are the attack methods and the anatomy of a web based application attack. This paper explores a number of methods for detection and prevention of vulnerabilities in the web application. This research suggests a more effective method for reducing this category of web vulnerabilities. Additionally, it offers the finest defence against the a for mentioned threats.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114439226","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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