Hybrid Neuro-Fuzzy Classification Algorithm for Social Network

Sharma, Anu; Sharma, M.K; Dwivedi, R.K

doi:10.35940/ijeat.f8537.088619

Cited by 4 publications

(1 citation statement)

References 3 publications

(4 reference statements)

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“…It also allows users to stay in touch with friends, exchange personal data, create personal pro les, and browse other people's pro les and connections [10]. Some of the most popular and frequently used social network sites are Twitter, Facebook, and MySpace [40] [41]. The greater majority of the general public however, uses Twitter to share information [1] [11].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prospects in Social Media and Cloud Data Mining and Analytics

Tao

Zhou

Jawawi

et al. 2022

Preprint

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Social media has become a significant platform not only for communication, but also for business and marketing of content and services. These activities involve large volumes of data with different patterns that have to be harnessed for businesses or users to maximize their meaning and make relevant brand decisions. Machine learning algorithms and tools like Natural language Processing (NLP) facilitate the system’s ability to recognize, extract cloud data patterns, and classify them in a more realistic way. The previous method only made mere literal guesses to aid automated data tracking and decision-making. The aim of this study was to carry out social media data analytics using machine learning tools. In this study, we proposed novel Naïve Bayes and Maximum Entropy classifiers. These classifiers were used to conduct a sentiment analysis to categorize and analyze messages collected from Twitter in order to determine whether they are positive, negative or non-partisan, and ultimately get the targeted output. The experimental results were successful and the machine learning tools were accurate in determining the nature of the Twitter messages. The study demonstrates the capacity of machine learning algorithms in conducting actionable social media data mining and analytics.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Prospects in Social Media and Cloud Data Mining and Analytics

Tao

Zhou

Jawawi

et al. 2022

Preprint

View full text Add to dashboard Cite

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Diabetes Predication Analysis using Supervised Machine Learning Algorithm

Agarwal

Sharma

Gupta

et al. 2022

2022 11th International Conference on System Modeling &Amp; Advancement in Research Trends (SMART)

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Brain Tumor Detection From MRI Images Using Deep Learning Techniques

Sharma

2024

The Ethical Frontier of AI and Data Analysis

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Machine learning and deep learning algorithms are utilized to identify brain tumors in a number of research papers. When these algorithms are applied to MRI images, it takes exceedingly slight time to expect a brain tumor, and the increased accuracy makes it easier to treat patients. The performance of the hybrid Convolution Neural Network (CNN) used in the proposed work to detect the existence of brain tumours is examined. In this study, we suggested a hybrid convolutional neural network followed by deep learning techniques using 2D magnetic resonance brain pictures, segment brain tumors (MRI). In our research, hybrid CNN achieved an accuracy of 98.73%, outperforming the results so far.

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Hybrid Neuro-Fuzzy Classification Algorithm for Social Network

Cited by 4 publications

References 3 publications

Machine Learning Prospects in Social Media and Cloud Data Mining and Analytics

Machine Learning Prospects in Social Media and Cloud Data Mining and Analytics

Diabetes Predication Analysis using Supervised Machine Learning Algorithm

Brain Tumor Detection From MRI Images Using Deep Learning Techniques

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