A Novel Machine Learning Technique towards Predicting the Sale of Washing Machines in a Small Organization

Gopal, Madhwaraj Kango

doi:10.37200/ijpr/v24i5/pr2020697

Cited by 4 publications

(2 citation statements)

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“…Viswanath et al [4] performed a case study to predict the number of deaths due to dengue disease. Madhwaraj et al [5] identified a novel machine learning idea to predict the sale of washing machines in an organization. Machine learning algorithms are used for a variety of purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Traffic Congestion using Graph Convolutional Networks

Gopal,

Asha,

Prasad

et al. 2023

Advances in Computer Science Research

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Congestion in a network can cause data packet losses, delays, and reduced network performance. To prevent congestion, network engineers must be able to accurately predict and manage network traffic. In this research paper, we explore the use of Graph Convolutional Networks (GCN) for predicting congestion in a network. GCN is a type of deep learning algorithm that can analyze complex network structures to predict the behavior of nodes in a network. With GCN, predicting congestion in a network, identification of potential congested areas becomes a reality. Proactive measures to prevent congestion from occurring is also been attempted in this research work. The results of our experiments demonstrate that GCN outperforms other conventional machine learning techniques in predicting network congestion with high accuracy and precision using ReLU6, which was the most suitable activation function for implementing the model. This research also demonstrates the potential of using deep learning algorithms such as GCN to improve network management and optimize network performance.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling Traffic Congestion using Graph Convolutional Networks

Gopal,

Asha,

Prasad

et al. 2023

Advances in Computer Science Research

View full text Add to dashboard Cite

show abstract

“…To estimate the number of deaths brought on by dengue fever, Viswanath et al [1] conducted a case study. An innovative machinelearning concept was found by Madhwaraj et al [2] to forecast the sale of washing machines inside an organization. There are several uses for machine learning algorithms.…”

Section: IImentioning

confidence: 99%

Tool to Detect Fake Accounts in Twitter

Gopal,

Asha,

Saju

et al. 2023

Advances in Computer Science Research

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On the internet social media platforms are growing in popularity, and that popularity has raised increasing questions about security and privacy. Users of social networks face serious security risks due to fake and copied profiles. One major issue is the cloning of user profiles, when duplicate profiles are created using stolen user data and then utilized to harm the original profile owner. Threats like phishing, stalking, spamming, and others are also used to accomplish a variety of goals. A fake profile is one that is made on a social networking site using the name of an organization or person that does not exist and participates in destructive activities. In this study, a new tool is created that uses machine learning methods to verify user identification. People who utilize fake accounts may be recognized as having fake profiles in one of three ways: the number of abuse reports, daily comments, or rejected friend requests. Data from Twitter was used in a case study. The Random Forest algorithm and the Support vector machine approach offered a greater projected accuracy when detecting whether a user was a fraudulent or genuine user compared to other machine learning methods.

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