SGBBA: An Efficient Method for Prediction System in Machine Learning using Imbalance Dataset

Islam, Saiful; Sara, Umme; Kawsar, Abu; Rahman, Asheq; Kundu, Dipanjali; Dipta, Diganta Das; Karim, Abdul; Hasan, Mahedi

doi:10.14569/ijacsa.2021.0120351

Cited by 17 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this regard, people are utilizing various technologies into the health care systems such as SDN, Blockchain, etc [234,235]. However, it is an advanced machine learning method that needs large datasets [233,236]. A suitable dataset is essential for such problems.…”

Section: Open Issuesmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

Self Cite

View full text Add to dashboard Cite

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

show abstract

Section: Open Issuesmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the information exchanged with the controller(s), the IDS can guide the update of the flow table to avoid malicious threats conducted by an attacker. To attain this goal, some researchers propose to check for the entropy of the IP addresses to discover anomalies regarding the users, and then analyze the flow table to figure out some useful features to build machine learning (ML) models for future predictions on unseen data [106,107].…”

Section: Security Management In Sdnmentioning

confidence: 99%

On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives

et al. 2022

Self Cite

View full text Add to dashboard Cite

Blockchain (BC) and software-defined networking (SDN) are leading technologies which have recently found applications in several network-related scenarios and have consequently experienced a growing interest in the research community. Indeed, current networks connect a massive number of objects over the Internet and in this complex scenario, to ensure security, privacy, confidentiality, and programmability, the utilization of BC and SDN have been successfully proposed. In this work, we provide a comprehensive survey regarding these two recent research trends and review the related state-of-the-art literature. We first describe the main features of each technology and discuss their most common and used variants. Furthermore, we envision the integration of such technologies to jointly take advantage of these latter efficiently. Indeed, we consider their group-wise utilization—named BC–SDN—based on the need for stronger security and privacy. Additionally, we cover the application fields of these technologies both individually and combined. Finally, we discuss the open issues of reviewed research and describe potential directions for future avenues regarding the integration of BC and SDN. To summarize, the contribution of the present survey spans from an overview of the literature background on BC and SDN to the discussion of the benefits and limitations of BC–SDN integration in different fields, which also raises open challenges and possible future avenues examined herein. To the best of our knowledge, compared to existing surveys, this is the first work that analyzes the aforementioned aspects in light of a broad BC–SDN integration, with a specific focus on security and privacy issues in actual utilization scenarios.

show abstract

“…The various advantages of DL and ML, such as improved performance, a lateral learning model with incorporated feature learning, and the aptitude to deal with intricate, diverse information, among others, could be advantageous to the healthcare industry. Additionally, the sparse, noisy, heterogeneous, and time-dependent nature of healthcare data presents a number of challenges for the DL and ML research community [16] . Additionally, better methods and tools are required to integrate DL into clinical decision-support functioning.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Rahman,

Debnath,

Kundu

et al. 2024

AIMSPH

View full text Add to dashboard Cite

<abstract> <p>In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.</p> </abstract>

show abstract

SGBBA: An Efficient Method for Prediction System in Machine Learning using Imbalance Dataset

Cited by 17 publications

References 12 publications

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Contact Info

Product

Resources

About