In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.
{"title":"Decoding the Threat Landscape : ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks","authors":"Polra Victor Falade","doi":"10.32628/cseit2390533","DOIUrl":"https://doi.org/10.32628/cseit2390533","url":null,"abstract":"In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135746063","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}
None Sai Prasad Baswoju, None Y Latha, None Ravindra Changala, None Annapurna Gummadi
Food spoilage is a pervasive issue that contributes to food waste and poses significant economic and environmental challenges worldwide. To combat this problem, we propose the development of a Convolutional Neural Network (CNN) model capable of predicting and preventing food spoilage. This paper outlines the methodology, data collection, model architecture, and evaluation of our CNN-based solution, which aims to assist consumers, retailers, and food producers in minimizing food waste. Researchers are working on innovative techniques to preserve the quality of food in an effort to extend its shelf life since grains are prone to spoiling as a result of precipitation, humidity, temperature, and a number of other factors. In order to maintain current standards of food quality, effective surveillance systems for food deterioration are needed. To monitor food quality and control home storage systems, we have created a prototype. To start, we used a Convolutional Neural Network (CNN) model to identify the different types of fruits and vegetables. The suggested system then uses sensors and actuators to check the amount of food spoiling by monitoring the gas emission level, humidity level, and temperature of fruits and vegetables. Additionally, this would regulate the environment and, to the greatest extent feasible, prevent food spoiling. Additionally, based on the freshness and condition of the food, a message alerting the client to the food decomposition level is delivered to their registered cell numbers. The model used turned out to have a 96.3% accuracy rate.
{"title":"Development of CNN Model to Avoid Food Spoiling Level","authors":"None Sai Prasad Baswoju, None Y Latha, None Ravindra Changala, None Annapurna Gummadi","doi":"10.32628/cseit2390536","DOIUrl":"https://doi.org/10.32628/cseit2390536","url":null,"abstract":"Food spoilage is a pervasive issue that contributes to food waste and poses significant economic and environmental challenges worldwide. To combat this problem, we propose the development of a Convolutional Neural Network (CNN) model capable of predicting and preventing food spoilage. This paper outlines the methodology, data collection, model architecture, and evaluation of our CNN-based solution, which aims to assist consumers, retailers, and food producers in minimizing food waste. Researchers are working on innovative techniques to preserve the quality of food in an effort to extend its shelf life since grains are prone to spoiling as a result of precipitation, humidity, temperature, and a number of other factors. In order to maintain current standards of food quality, effective surveillance systems for food deterioration are needed. To monitor food quality and control home storage systems, we have created a prototype. To start, we used a Convolutional Neural Network (CNN) model to identify the different types of fruits and vegetables. The suggested system then uses sensors and actuators to check the amount of food spoiling by monitoring the gas emission level, humidity level, and temperature of fruits and vegetables. Additionally, this would regulate the environment and, to the greatest extent feasible, prevent food spoiling. Additionally, based on the freshness and condition of the food, a message alerting the client to the food decomposition level is delivered to their registered cell numbers. The model used turned out to have a 96.3% accuracy rate.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136198691","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}
During the COVID-19 epidemic phishing dodges increased in frequency mostly the links provided current updates about COVID-19 hence it became easy to trick the victims. Many research studies suggest several solutions to prevent those attacks but still phishing assaults upsurge. There is no only way to perform phishing attacks through web links attackers also perform attacks through electronic mail. This study aims to propose an Effective Model using Ensemble Classifiers to predict phishing using COVID-19-themed emails and Web Links. Our study comprises two types of Datasets. Dataset 1 for web links and Dataset 2 for email. Dataset 1 contains a textual dataset while Dataset 2 contains images that were downloaded from different sources. We select ensemble classifiers including, Random Forest (RF), Ada Boost, Bagging, ExtraTree (ET), and Gradient Boosting (GB). During the analysis, we observed that Dataset 1 achieves the highest accuracy rate as compared to Dataset 2 which is 88.91%. The ET classifier performs with an accuracy rate of 88.91%, a precision rate of 89%, a recall rate of 89%, and an f1 score of 89% which is better as compared to other classifiers over both datasets. Interesting concepts were found during the study.
{"title":"Machine Learning-Based Detection of Phishing in COVID-19 Theme-Related Emails and Web Links","authors":"None Usman Ali, None Dr. Isma Farah Siddiqui","doi":"10.32628/cseit2390563","DOIUrl":"https://doi.org/10.32628/cseit2390563","url":null,"abstract":"During the COVID-19 epidemic phishing dodges increased in frequency mostly the links provided current updates about COVID-19 hence it became easy to trick the victims. Many research studies suggest several solutions to prevent those attacks but still phishing assaults upsurge. There is no only way to perform phishing attacks through web links attackers also perform attacks through electronic mail. This study aims to propose an Effective Model using Ensemble Classifiers to predict phishing using COVID-19-themed emails and Web Links. Our study comprises two types of Datasets. Dataset 1 for web links and Dataset 2 for email. Dataset 1 contains a textual dataset while Dataset 2 contains images that were downloaded from different sources. We select ensemble classifiers including, Random Forest (RF), Ada Boost, Bagging, ExtraTree (ET), and Gradient Boosting (GB). During the analysis, we observed that Dataset 1 achieves the highest accuracy rate as compared to Dataset 2 which is 88.91%. The ET classifier performs with an accuracy rate of 88.91%, a precision rate of 89%, a recall rate of 89%, and an f1 score of 89% which is better as compared to other classifiers over both datasets. Interesting concepts were found during the study.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136198694","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}
Classification, a crucial aspect of machine learning, revolves around the meticulous analysis of data. However, the complexity of diverse life forms on Earth poses a challenge in distinguishing species that share similar attributes. The iris flower, with its subspecies exemplifies this challenge. The aim of the paper is to develop a methodology that not only enhances classification accuracy but also effectively addresses computational efficiency, facilitating faster and more practical categorization of iris patterns. This novel approach named Piecewise Linear Approximation based SVM (PLA-SVM) is applied to flower species classification and is benchmarked against alternative machine learning techniques. Implementation is carried out utilizing MATLAB – GUROBI interface of and GUROBI Solver. The performance metrics such as accuracy, precision, F1 score and ROC – AUC Curve are used to compare proposed algorithm performance. This comprehensive analysis enables a comparative study of diverse algorithms, ultimately validating the proposed PLA-SVM technique using the Iris dataset. The numerical implementation results shows that the PLASVM outperforms the existing standard classifiers in terms of different performance matrices.
{"title":"Piecewise Linear Approximation-Driven Primal SVM Approach for Improved Iris Classification Efficiency","authors":"Shital Solanki, Dr. Ramesh Prajapati","doi":"10.32628/cseit12390542","DOIUrl":"https://doi.org/10.32628/cseit12390542","url":null,"abstract":"Classification, a crucial aspect of machine learning, revolves around the meticulous analysis of data. However, the complexity of diverse life forms on Earth poses a challenge in distinguishing species that share similar attributes. The iris flower, with its subspecies exemplifies this challenge. The aim of the paper is to develop a methodology that not only enhances classification accuracy but also effectively addresses computational efficiency, facilitating faster and more practical categorization of iris patterns. This novel approach named Piecewise Linear Approximation based SVM (PLA-SVM) is applied to flower species classification and is benchmarked against alternative machine learning techniques. Implementation is carried out utilizing MATLAB – GUROBI interface of and GUROBI Solver. The performance metrics such as accuracy, precision, F1 score and ROC – AUC Curve are used to compare proposed algorithm performance. This comprehensive analysis enables a comparative study of diverse algorithms, ultimately validating the proposed PLA-SVM technique using the Iris dataset. The numerical implementation results shows that the PLASVM outperforms the existing standard classifiers in terms of different performance matrices.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"280 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922521","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 potential of using artificial intelligence in education to enhance learning, assist teachers and fuel more effective individualized learning is exciting, but also a bit challenging. To even have an intelligent conversation about AI in education, one must first push past imaginary science-fiction scenarios of computers and robots teaching our children, replacing teachers and reducing the human element from what is a fundamentally human activity. AI can automate grading so that the tutor can have more time to teach. AI chatbot can communicate with students as a teaching assistant. This research paper focuses on modelling of AI ingredients in framework of education. AI in future can work as a personal virtual tutor for students, which will be easily accessible at any time and any place.
{"title":"Review of Artificial Intelligence Applications and Modelling AI Framework in Education System","authors":"None Patel Karika Digesh","doi":"10.32628/cseit2390542","DOIUrl":"https://doi.org/10.32628/cseit2390542","url":null,"abstract":"The potential of using artificial intelligence in education to enhance learning, assist teachers and fuel more effective individualized learning is exciting, but also a bit challenging. To even have an intelligent conversation about AI in education, one must first push past imaginary science-fiction scenarios of computers and robots teaching our children, replacing teachers and reducing the human element from what is a fundamentally human activity. AI can automate grading so that the tutor can have more time to teach. AI chatbot can communicate with students as a teaching assistant. This research paper focuses on modelling of AI ingredients in framework of education. AI in future can work as a personal virtual tutor for students, which will be easily accessible at any time and any place.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136198693","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}
Machine learning is one of the essential technologies that is prevailing nowadays in almost every sector of business and education. People are becoming more advanced and developed gaining higher levels of technologies and learning data. Machine learning plays a key role in monitoring and facilitating various aspects of crowd intelligence which includes identification of a good level of workflow, collecting responses from individuals regarding workflow, and testing of various methods that can enable in crowdsourcing of the task. Various methods are adopted under machine learning to improvise and increase the demanded track of career and growth pace of business firms. One of the best methods which are available for analysing data and used by professionals is crowd-powered machine learning which in turn facilitates in automation of the building of analytical models. The following research is also based on a similar aspect in which discussion is been made regarding crowd-powered machine learning as well and an evaluation of the intelligent management of crowd-powered machine learning is also ascertained. Furthermore, the research also discusses the role played by machine intelligence in the management of crowd intelligence in AI. The research has also highlighted the various methods as well as techniques in order to understand the role of machine learning in the effective management of crowd intelligence.
{"title":"Role of Machine Learning in Managing Crowd Intelligence","authors":"None Mohit Suthar, None Sunil Sharma","doi":"10.32628/cseit2390525","DOIUrl":"https://doi.org/10.32628/cseit2390525","url":null,"abstract":"Machine learning is one of the essential technologies that is prevailing nowadays in almost every sector of business and education. People are becoming more advanced and developed gaining higher levels of technologies and learning data. Machine learning plays a key role in monitoring and facilitating various aspects of crowd intelligence which includes identification of a good level of workflow, collecting responses from individuals regarding workflow, and testing of various methods that can enable in crowdsourcing of the task. Various methods are adopted under machine learning to improvise and increase the demanded track of career and growth pace of business firms. One of the best methods which are available for analysing data and used by professionals is crowd-powered machine learning which in turn facilitates in automation of the building of analytical models. The following research is also based on a similar aspect in which discussion is been made regarding crowd-powered machine learning as well and an evaluation of the intelligent management of crowd-powered machine learning is also ascertained. Furthermore, the research also discusses the role played by machine intelligence in the management of crowd intelligence in AI. The research has also highlighted the various methods as well as techniques in order to understand the role of machine learning in the effective management of crowd intelligence.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136107532","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}
In addition to their vulnerability, the complexity of the operations, and the high expenses, disorders of the brain are one of the most challenging diseases to treat. However, because the outcome is unpredictable, the procedure itself does not need to be successful. One of the most prevalent brain diseases in adults, hypertension, can cause varying degrees of memory loss and forgetfulness. Depending on each patient's situation. For these reasons, it's crucial to define memory loss, determine the patient's level of decline, and determine his brain MRI scans are used to identify Alzheimer's disease. In this thesis, we discuss methods and approaches for diagnosing Alzheimer's disease using deep learning. The suggested approach is utilized to enhance patient care, lower expenses, and enable quick and accurate analysis in sizable investigations. Modern deep learning techniques have lately successfully demonstrated performance at the level of a human in various domains, including medical image processing. We propose a deep convolutional network for diagnosing Alzheimer's disease based on the analysis of brain MRI data. Our model outperforms other models for early detection of current techniques because it can distinguish between different stages of Alzheimer's disease.
{"title":"A System for Diagnosing Alzheimer’s Disease from Brain MRI Images Using Deep Learning Algorithm","authors":"None S. Neelavthi, None P. Arunkumar","doi":"10.32628/cseit2390530","DOIUrl":"https://doi.org/10.32628/cseit2390530","url":null,"abstract":"In addition to their vulnerability, the complexity of the operations, and the high expenses, disorders of the brain are one of the most challenging diseases to treat. However, because the outcome is unpredictable, the procedure itself does not need to be successful. One of the most prevalent brain diseases in adults, hypertension, can cause varying degrees of memory loss and forgetfulness. Depending on each patient's situation. For these reasons, it's crucial to define memory loss, determine the patient's level of decline, and determine his brain MRI scans are used to identify Alzheimer's disease. In this thesis, we discuss methods and approaches for diagnosing Alzheimer's disease using deep learning. The suggested approach is utilized to enhance patient care, lower expenses, and enable quick and accurate analysis in sizable investigations. Modern deep learning techniques have lately successfully demonstrated performance at the level of a human in various domains, including medical image processing. We propose a deep convolutional network for diagnosing Alzheimer's disease based on the analysis of brain MRI data. Our model outperforms other models for early detection of current techniques because it can distinguish between different stages of Alzheimer's disease.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"367 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136107531","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}
A Student life is all about gaining knowledge and implementing it. We have many competitive exams out there and students explore themselves and chooses the path as per their interests and skills. Entrance Q-Bank is the best to proceed. It helps you to find previous years question papers where you could test and practice accordingly It is nothing but treasure, so if you can get your hands on previous year papers then it is no less than a hitting a lottery. It manages your time efficiently. It also makes you confident during exams. We choose this as our project because, instead of laying hands on different websites, having an app makes it more advantageous and time saving. On the whole, we hope to implement an app which saves time and make you more confident. We created this application with Android Studio, XML for the User Interface and Java for the backend.
{"title":"Entrance-Q bank using Mobile Application Development","authors":"None Manasvi Malhar Sudershan","doi":"10.32628/cseit2390532","DOIUrl":"https://doi.org/10.32628/cseit2390532","url":null,"abstract":"A Student life is all about gaining knowledge and implementing it. We have many competitive exams out there and students explore themselves and chooses the path as per their interests and skills. Entrance Q-Bank is the best to proceed. It helps you to find previous years question papers where you could test and practice accordingly It is nothing but treasure, so if you can get your hands on previous year papers then it is no less than a hitting a lottery. It manages your time efficiently. It also makes you confident during exams. We choose this as our project because, instead of laying hands on different websites, having an app makes it more advantageous and time saving. On the whole, we hope to implement an app which saves time and make you more confident. We created this application with Android Studio, XML for the User Interface and Java for the backend.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136107533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper highlights the security issues associated with credit cards and underscores the crucial role of encryption in mitigating the risk of credit or debit card data theft. Credit card encryption encompasses safeguarding the card itself, securing the terminal used for card scanning, and ensuring the protection of card information during transmission between the terminal and a backend computer system. The encryption mechanism is specifically engineered to validate and limit access to card security features. In our project, we developed a web application using VS Code, employing HTML for the frontend and PHP for the backend, and implemented AES encryption as a robust security measure.
{"title":"Enhancing Card Transaction Security through Cyber Security","authors":"None Manasvi Malhar Sudershan, None Vasundhara Rao, None SVS Harshitha, None Swetha Mukka, None Sai Varshan","doi":"10.32628/cseit2390531","DOIUrl":"https://doi.org/10.32628/cseit2390531","url":null,"abstract":"This paper highlights the security issues associated with credit cards and underscores the crucial role of encryption in mitigating the risk of credit or debit card data theft. Credit card encryption encompasses safeguarding the card itself, securing the terminal used for card scanning, and ensuring the protection of card information during transmission between the terminal and a backend computer system. The encryption mechanism is specifically engineered to validate and limit access to card security features. In our project, we developed a web application using VS Code, employing HTML for the frontend and PHP for the backend, and implemented AES encryption as a robust security measure.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136107534","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}
Sign language serves as a vital means of communication for the deaf and hard of hearing community. However, identifying sign language poses a significant challenge due to its complexity and the lack of a standardized global framework. Recent advances in machine learning, particularly Long Short-Term Memory (LSTM) algorithms, offer promise in the field of sign language gesture recognition. This research introduces an innovative method that leverages LSTM, a type of recurrent neural network designed for processing sequential input. Our goal is to create a highly accurate system capable of anticipating and reproducing sign language motions with precision. LSTM's unique capabilities enhance the recognition of complex gestures by capturing the temporal relationships and fine details inherent in sign language. The results of this study demonstrate that LSTM-based approaches outperform existing state-of-the-art techniques, highlighting the effectiveness of LSTM in sign language recognition and their potential to facilitate communication between the deaf and hearing communities.
{"title":"Enhancing Accessibility with LSTM-Based Sign Language Detection","authors":"None Azees Abdul, None Adithya Valapa, None Abdul Kayom Md Khairuzzaman","doi":"10.32628/cseit2390517","DOIUrl":"https://doi.org/10.32628/cseit2390517","url":null,"abstract":"Sign language serves as a vital means of communication for the deaf and hard of hearing community. However, identifying sign language poses a significant challenge due to its complexity and the lack of a standardized global framework. Recent advances in machine learning, particularly Long Short-Term Memory (LSTM) algorithms, offer promise in the field of sign language gesture recognition. This research introduces an innovative method that leverages LSTM, a type of recurrent neural network designed for processing sequential input. Our goal is to create a highly accurate system capable of anticipating and reproducing sign language motions with precision. LSTM's unique capabilities enhance the recognition of complex gestures by capturing the temporal relationships and fine details inherent in sign language. The results of this study demonstrate that LSTM-based approaches outperform existing state-of-the-art techniques, highlighting the effectiveness of LSTM in sign language recognition and their potential to facilitate communication between the deaf and hearing communities.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136264638","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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