Today's digital environment, Multi-user authentication plays a crucial role in ensuring data integrity and confidentiality, emphasizing its importance of reliable and secure data storage in cloud computing environments. The exploration extends to the strategies for implementing secure multi-user authentication, encompassing aspects such as password policies, biometric verification, encryption, role-based access control (RBAC), and multi-factor authentication (MFA). The issue of reliable data storage is covered in further detail, on the importance of data availability and integrity. Real-world applications of multi-user authentication and reliable data storage are examine. The paper elucidates how these applications enhance overall security, mitigating risks associated with unauthorized access and cyber threats.The paper concludes by integration of multi-user authentication and reliable data storage is explored through considerations the critical role of multi-user authentication in ensuring reliable data storage in cloud computing such as secure API access, token-based authentication, and adherence to security best practices. Challenges in user authentication are addressed, with solutions proposed for seamless access across cloud platforms, including the adoption of Single Sign-On (SSO), multi-factor authentication, regular security audits, collaboration with cloud security experts, and user education and training. The synthesis of challenges, benefits, drawbacks, and implementation strategies provides organizations with a comprehensive guide for enhancing their data security measures.
在当今的数字环境中，多用户身份验证在确保数据完整性和保密性方面发挥着至关重要的作用，强调了其在云计算环境中可靠和安全数据存储的重要性。本讲座探讨了实施安全多用户身份验证的策略，包括密码策略、生物特征验证、加密、基于角色的访问控制（RBAC）和多因素身份验证（MFA）等方面。此外，还进一步详细介绍了可靠的数据存储问题，以及数据可用性和完整性的重要性。 论文研究了多用户身份验证和可靠数据存储在现实世界中的应用。论文最后通过多用户身份验证在确保云计算中可靠数据存储方面的关键作用，如安全 API 访问、基于令牌的身份验证以及遵守安全最佳实践等方面的考虑，探讨了多用户身份验证与可靠数据存储的整合。针对用户身份验证的挑战，提出了跨云平台无缝访问的解决方案，包括采用单点登录（SSO）、多因素身份验证、定期安全审计、与云安全专家合作以及用户教育和培训。本书综合了各种挑战、益处、弊端和实施策略，为企业加强数据安全措施提供了全面的指导。
{"title":"Multi User Authentication for Reliable Data Storage in Cloud Computing","authors":"Richa Shah, Shatendra Kumar Dubey","doi":"10.32628/cseit2410138","DOIUrl":"https://doi.org/10.32628/cseit2410138","url":null,"abstract":"Today's digital environment, Multi-user authentication plays a crucial role in ensuring data integrity and confidentiality, emphasizing its importance of reliable and secure data storage in cloud computing environments. The exploration extends to the strategies for implementing secure multi-user authentication, encompassing aspects such as password policies, biometric verification, encryption, role-based access control (RBAC), and multi-factor authentication (MFA). The issue of reliable data storage is covered in further detail, on the importance of data availability and integrity. Real-world applications of multi-user authentication and reliable data storage are examine. The paper elucidates how these applications enhance overall security, mitigating risks associated with unauthorized access and cyber threats.The paper concludes by integration of multi-user authentication and reliable data storage is explored through considerations the critical role of multi-user authentication in ensuring reliable data storage in cloud computing such as secure API access, token-based authentication, and adherence to security best practices. Challenges in user authentication are addressed, with solutions proposed for seamless access across cloud platforms, including the adoption of Single Sign-On (SSO), multi-factor authentication, regular security audits, collaboration with cloud security experts, and user education and training. The synthesis of challenges, benefits, drawbacks, and implementation strategies provides organizations with a comprehensive guide for enhancing their data security measures. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"62 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253062","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 sentiment analysis, emotions refer to the subjective feelings expressed in a text or speech that can be classified as positive, negative or neutral. Emotions are an important aspect of sentiment analysis because they provide insights into the attitudes, opinions and behaviors of individuals toward a particular topic or entity. The emergence of digital humanities has allowed for a more computational approach to understanding emotions in literature. The passage provides an overview of existing research in this area and understanding the emotionality involved in text. Throughout this survey, it has been demonstrated that sentiment and emotion analysis is increasingly attracting attention within the field of digital humanities, particularly in computational literary studies.
{"title":"A Review on Sentiment and Emotion Analysis for Computational Literary Studies","authors":"Nasrullah Makhdom, H. N. Verma, Arun Kumar Yadav","doi":"10.32628/cseit241029","DOIUrl":"https://doi.org/10.32628/cseit241029","url":null,"abstract":"In sentiment analysis, emotions refer to the subjective feelings expressed in a text or speech that can be classified as positive, negative or neutral. Emotions are an important aspect of sentiment analysis because they provide insights into the attitudes, opinions and behaviors of individuals toward a particular topic or entity. The emergence of digital humanities has allowed for a more computational approach to understanding emotions in literature. The passage provides an overview of existing research in this area and understanding the emotionality involved in text. Throughout this survey, it has been demonstrated that sentiment and emotion analysis is increasingly attracting attention within the field of digital humanities, particularly in computational literary studies. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"23 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254360","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}
Embodies a particular approach with the devising set out to enrich the quality of the city living experience incorporating technology and real human relationships in applying information in the city. The research journey is going to trace dynamic pathways underpinning in software development through powerful tools like Android Studio among others that helps in designing simple and user-friendly interfaces that easily adjust themselves to the diversity of the residents who live in cities and visitors come to the city. It now focuses to build proper links of implementation not on the technological strides but only by increasing the spotlight. Reliability as well as availability is ensuring that hustling has a strong back-end that would handle the data smoothly by the use of applications like Firebase and Room Persistence. Added to it, the approach makes use of offline capabilities as well part of validation of strict security precursors for performing through real-user trials while designing the likes of the application in conformance to the changing user preferences of the urban communities. This methodology visualizes transformative fostering of people-centric cities in successfully intricacy technology by richness of human experiences within innovative city information application, and elevated urbanlifestyles.
该项目采用一种特殊的方法，旨在丰富城市生活体验的质量，在城市信息应用中融入技术和真实的人际关系。研究历程将通过强大的工具（如 Android Studio 等）追踪软件开发的动态路径，这些工具有助于设计简单、用户友好的界面，轻松适应城市居民和游客的多样性。现在的重点是建立适当的实施环节，而不是技术上的进步，只是增加关注点。通过使用 Firebase 和 Room Persistence 等应用程序，可靠性和可用性确保了喧嚣拥有一个强大的后端，能够顺利处理数据。除此以外，该方法还利用离线功能，以及通过真实用户试验验证严格的安全先决条件，同时根据城市社区不断变化的用户偏好设计应用程序。该方法通过创新的城市信息应用中丰富的人类体验和提升的城市生活方式，成功地将复杂的技术转化为以人为本的城市。
{"title":"Metropolitan Marvels : To Forge Seamless Possibilities for Urban Discoverability and Connectivity","authors":"Divya Shree B, Disha R, Ms. Sreelatha, Shivangi Vishwakarma","doi":"10.32628/cseit241028","DOIUrl":"https://doi.org/10.32628/cseit241028","url":null,"abstract":"Embodies a particular approach with the devising set out to enrich the quality of the city living experience incorporating technology and real human relationships in applying information in the city. The research journey is going to trace dynamic pathways underpinning in software development through powerful tools like Android Studio among others that helps in designing simple and user-friendly interfaces that easily adjust themselves to the diversity of the residents who live in cities and visitors come to the city. It now focuses to build proper links of implementation not on the technological strides but only by increasing the spotlight. Reliability as well as availability is ensuring that hustling has a strong back-end that would handle the data smoothly by the use of applications like Firebase and Room Persistence. Added to it, the approach makes use of offline capabilities as well part of validation of strict security precursors for performing through real-user trials while designing the likes of the application in conformance to the changing user preferences of the urban communities. This methodology visualizes transformative fostering of people-centric cities in successfully intricacy technology by richness of human experiences within innovative city information application, and elevated urbanlifestyles. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"11 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261953","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 study investigates the efficacy of various machine learning algorithms for detecting image forgery, a prevalent issue in the realm of digital media manipulation. The research focuses on assessing the performance of these algorithms in accurately identifying instances of image tampering, aiming to contribute valuable insights to the field of digital forensics. The evaluation encompasses a diverse set of machine learning techniques, including but not limited to convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees. Through rigorous experimentation and comparative analysis, the research aims to discern the strengths and limitations of each algorithm in the context of image forgery detection. The findings of this study hold significance for enhancing the capabilities of digital forensics tools, thereby aiding in the mitigation of fraudulent activities, and ensuring the integrity of visual content in the digital' domain.
{"title":"Exploring the Effectiveness of Machine Learning Algorithms in Image Forgery Detection","authors":"Niyati Patel, Premal J.Patel","doi":"10.32628/cseit2390669","DOIUrl":"https://doi.org/10.32628/cseit2390669","url":null,"abstract":"This study investigates the efficacy of various machine learning algorithms for detecting image forgery, a prevalent issue in the realm of digital media manipulation. The research focuses on assessing the performance of these algorithms in accurately identifying instances of image tampering, aiming to contribute valuable insights to the field of digital forensics. The evaluation encompasses a diverse set of machine learning techniques, including but not limited to convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees. Through rigorous experimentation and comparative analysis, the research aims to discern the strengths and limitations of each algorithm in the context of image forgery detection. The findings of this study hold significance for enhancing the capabilities of digital forensics tools, thereby aiding in the mitigation of fraudulent activities, and ensuring the integrity of visual content in the digital' domain.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"48 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440869","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 comprehensive review explores the landscape of object detectors in the context of urban mobility for smart traffic management. With the increasing complexity of urban environments and the integration of intelligent transportation systems, the demand for accurate and efficient object detection algorithms has surged. This paper provides a thorough examination of state-of-the-art object detectors, evaluating their performance, strengths, and limitations in the specific context of urban mobility. The review encompasses a wide range of detectors, including traditional computer vision methods and modern deep learning approaches, discussing their applicability to real-world urban traffic scenarios. By synthesizing insights from diverse methodologies, this review aims to guide researchers, practitioners, and policymakers in selecting suitable object detectors for enhancing smart traffic management systems in urban settings.
{"title":"A Comprehensive Review on Object Detectors for Urban Mobility on Smart Traffic Management","authors":"Shivani Mistry, S. Degadwala","doi":"10.32628/cseit2361050","DOIUrl":"https://doi.org/10.32628/cseit2361050","url":null,"abstract":"This comprehensive review explores the landscape of object detectors in the context of urban mobility for smart traffic management. With the increasing complexity of urban environments and the integration of intelligent transportation systems, the demand for accurate and efficient object detection algorithms has surged. This paper provides a thorough examination of state-of-the-art object detectors, evaluating their performance, strengths, and limitations in the specific context of urban mobility. The review encompasses a wide range of detectors, including traditional computer vision methods and modern deep learning approaches, discussing their applicability to real-world urban traffic scenarios. By synthesizing insights from diverse methodologies, this review aims to guide researchers, practitioners, and policymakers in selecting suitable object detectors for enhancing smart traffic management systems in urban settings.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"461 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263454","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 comprehensive review delves into the realm of email spam classification, scrutinizing the efficacy of various machine learning methods employed in the ongoing battle against unwanted email communication. The paper synthesizes a wide array of research findings, methodologies, and performance metrics to provide a holistic perspective on the evolving landscape of spam detection. Emphasizing the pivotal role of machine learning in addressing the dynamic nature of spam, the review explores the strengths and limitations of popular algorithms such as Naive Bayes, Support Vector Machines, and neural networks. Additionally, it examines feature engineering, dataset characteristics, and evolving threats, offering insights into the challenges and opportunities within the field. With a focus on recent advancements and emerging trends, this review aims to guide researchers, practitioners, and developers in the ongoing pursuit of robust and adaptive email spam classification systems.
{"title":"A Comprehensive Review on Email Spam Classification with Machine Learning Methods","authors":"Prachi Bhatnagar, S. Degadwala","doi":"10.32628/cseit2361048","DOIUrl":"https://doi.org/10.32628/cseit2361048","url":null,"abstract":"This comprehensive review delves into the realm of email spam classification, scrutinizing the efficacy of various machine learning methods employed in the ongoing battle against unwanted email communication. The paper synthesizes a wide array of research findings, methodologies, and performance metrics to provide a holistic perspective on the evolving landscape of spam detection. Emphasizing the pivotal role of machine learning in addressing the dynamic nature of spam, the review explores the strengths and limitations of popular algorithms such as Naive Bayes, Support Vector Machines, and neural networks. Additionally, it examines feature engineering, dataset characteristics, and evolving threats, offering insights into the challenges and opportunities within the field. With a focus on recent advancements and emerging trends, this review aims to guide researchers, practitioners, and developers in the ongoing pursuit of robust and adaptive email spam classification systems.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"98 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139279838","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 review paper provides a comprehensive analysis of multi-class Distributed Denial of Service (DDoS) attack classification in the context of Internet of Things (IoT) environments. The exponential growth of IoT devices has introduced new challenges in securing networks against sophisticated DDoS attacks. In this study, we explore and evaluate various classification techniques and methodologies designed to identify and mitigate multi-class DDoS attacks in IoT ecosystems. The paper synthesizes existing research, highlights key advancements, and identifies gaps in the current literature, offering insights into the state-of-the-art approaches for enhancing the security posture of IoT systems.
{"title":"A Comprehensive Review on Multi-Class DDoS Attack Classification in IoT","authors":"Shivani Sinha, Sheshang Degadwala","doi":"10.32628/cseit2361053","DOIUrl":"https://doi.org/10.32628/cseit2361053","url":null,"abstract":"This review paper provides a comprehensive analysis of multi-class Distributed Denial of Service (DDoS) attack classification in the context of Internet of Things (IoT) environments. The exponential growth of IoT devices has introduced new challenges in securing networks against sophisticated DDoS attacks. In this study, we explore and evaluate various classification techniques and methodologies designed to identify and mitigate multi-class DDoS attacks in IoT ecosystems. The paper synthesizes existing research, highlights key advancements, and identifies gaps in the current literature, offering insights into the state-of-the-art approaches for enhancing the security posture of IoT systems.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281482","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 review paper provides a comprehensive analysis of the advancements in COVID-19 cough audio classification through deep learning techniques. With the ongoing global pandemic, there is a growing need for non-intrusive and rapid diagnostic tools, and the utilization of audio-based methods for COVID-19 detection has gained considerable attention. The paper systematically reviews and compares various deep learning models, methodologies, and datasets employed for COVID-19 cough audio classification. The effectiveness, challenges, and future directions of these approaches are discussed, shedding light on the potential of audio-based diagnostics in the context of the current public health crisis.
{"title":"A Comprehensive Review on COVID-19 Cough Audio Classification through Deep Learning","authors":"Praveen Gupta, S. Degadwala","doi":"10.32628/cseit2361049","DOIUrl":"https://doi.org/10.32628/cseit2361049","url":null,"abstract":"This review paper provides a comprehensive analysis of the advancements in COVID-19 cough audio classification through deep learning techniques. With the ongoing global pandemic, there is a growing need for non-intrusive and rapid diagnostic tools, and the utilization of audio-based methods for COVID-19 detection has gained considerable attention. The paper systematically reviews and compares various deep learning models, methodologies, and datasets employed for COVID-19 cough audio classification. The effectiveness, challenges, and future directions of these approaches are discussed, shedding light on the potential of audio-based diagnostics in the context of the current public health crisis.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281570","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 comprehensive review investigates the escalating concern of adversarial attacks on deep learning models, offering an extensive analysis of state-of-the-art detection techniques. Encompassing traditional machine learning methods and contemporary deep learning approaches, the review categorizes and evaluates various detection mechanisms while addressing challenges such as the need for benchmark datasets and interpretability. Emphasizing the crucial role of explaining ability and trustworthiness, the paper also explores emerging trends, including the integration of technologies like explainable artificial intelligence (XAI) and reinforcement learning. By synthesizing existing knowledge and outlining future research directions, this review serves as a valuable resource for researchers, practitioners, and stakeholders seeking a nuanced understanding of adversarial attack detection in deep learning.
{"title":"A Comprehensive Review on Adversarial Attack Detection Analysis in Deep Learning","authors":"Soni Kumari, S. Degadwala","doi":"10.32628/cseit2361054","DOIUrl":"https://doi.org/10.32628/cseit2361054","url":null,"abstract":"This comprehensive review investigates the escalating concern of adversarial attacks on deep learning models, offering an extensive analysis of state-of-the-art detection techniques. Encompassing traditional machine learning methods and contemporary deep learning approaches, the review categorizes and evaluates various detection mechanisms while addressing challenges such as the need for benchmark datasets and interpretability. Emphasizing the crucial role of explaining ability and trustworthiness, the paper also explores emerging trends, including the integration of technologies like explainable artificial intelligence (XAI) and reinforcement learning. By synthesizing existing knowledge and outlining future research directions, this review serves as a valuable resource for researchers, practitioners, and stakeholders seeking a nuanced understanding of adversarial attack detection in deep learning.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281047","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 comprehensive review delves into the application of deep learning techniques for the precise identification of papaya diseases. With the increasing importance of papaya as a major tropical fruit crop, the accurate and timely diagnosis of diseases is crucial for effective disease management. The paper synthesizes recent advancements in deep learning methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, applied to image-based disease identification in papaya plants. The review assesses the strengths and limitations of various deep learning models, explores the integration of multi-modal data sources, and evaluates the performance metrics employed for disease detection accuracy. Additionally, the study discusses challenges and future directions in leveraging deep learning for papaya disease identification, aiming to provide a comprehensive understanding of the current state and potential advancements in this critical agricultural domain.
{"title":"A Comprehensive Review on Deep Learning for Accurate Papaya Disease Identification","authors":"Monali Parmar, S. Degadwala","doi":"10.32628/cseit2361047","DOIUrl":"https://doi.org/10.32628/cseit2361047","url":null,"abstract":"This comprehensive review delves into the application of deep learning techniques for the precise identification of papaya diseases. With the increasing importance of papaya as a major tropical fruit crop, the accurate and timely diagnosis of diseases is crucial for effective disease management. The paper synthesizes recent advancements in deep learning methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, applied to image-based disease identification in papaya plants. The review assesses the strengths and limitations of various deep learning models, explores the integration of multi-modal data sources, and evaluates the performance metrics employed for disease detection accuracy. Additionally, the study discusses challenges and future directions in leveraging deep learning for papaya disease identification, aiming to provide a comprehensive understanding of the current state and potential advancements in this critical agricultural domain.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281533","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：