Mining social media text for disaster resource management using a feature selection based on forest optimization

Bhoi, Ashutosh; Balabantaray, Rakesh Chandra; Sahoo, Deepak; Dhiman, Gaurav; Khare, Manish; Narducci, Fabio; Kaur, Amandeep

doi:10.1016/j.cie.2022.108280

Cited by 19 publications

(3 citation statements)

References 40 publications

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“…This algorithm can handle classification and regression issues. One of the primary benefits of is that it can handle huge datasets with high dimensionality and non-linear variable interactions (Bhoi et al, 2022). It can also manage missing data and outliers and is less susceptible to overfitting than other decision tree-based algorithms, making it more accurate at predicting the outcomes of new data (Liu et al, 2020).…”

Section: Algorithm Type Descriptionmentioning

confidence: 99%

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Jain,

Dhupper,

Shrivastava

et al. 2023

Front. Environ. Sci.

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Globally, communities and governments face growing challenges from an increase in natural disasters and worsening weather extremes. Precision in disaster preparation is crucial in responding to these issues. The revolutionary influence that machine learning algorithms have in strengthening catastrophe preparation and response systems is thoroughly explored in this paper. Beyond a basic summary, the findings of our study are striking and demonstrate the sophisticated powers of machine learning in forecasting a variety of weather patterns and anticipating a range of natural catastrophes, including heat waves, droughts, floods, hurricanes, and more. We get practical insights into the complexities of machine learning applications, which support the enhanced effectiveness of predictive models in disaster preparedness. The paper not only explains the theoretical foundations but also presents practical proof of the significant benefits that machine learning algorithms provide. As a result, our results open the door for governments, businesses, and people to make wise decisions. These accurate predictions of natural catastrophes and emerging weather patterns may be used to implement pre-emptive actions, eventually saving lives and reducing the severity of the damage.

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Section: Algorithm Type Descriptionmentioning

confidence: 99%

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Jain,

Dhupper,

Shrivastava

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…In the prediction stage, some researchers use regression algorithms to find the correlation between the reference content and the group of generated tweets to arrive at a result close to the expected one. One of the main challenges for researchers in the filtering process is to identify irrelevant tweets in large volumes for disposal since relevant tweets are scarce [6][7] [8]. An approach used by many researchers involves organizing the terms used in tweets by adopting a classifier with textual characteristics that restrict the group of words or use only keywords depending on the analysis [9][10] [11] [12].…”

Section: Introductionmentioning

confidence: 99%

A Metrics-Based Approach to Metadata Classification Applied in a Recommendation System

Santos

Vales

Junior

2023

Preprint

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In this article we have presented a classification model for metadata extracted from tweets using collaborative filters, proposing a metadata recommendation system at the knowledge level. Due to the limited resources available for tagging in social media, the quality of metadata used for this practice often poses interpretation issues when selecting, resulting in a loss of engagement due to flaws in term formation or a lack of understanding of the shared metadata within the social network. In this study, we proposed a selection algorithm that filters and classifies metadata based on metrics that measure the collective intelligence aggregated in the metadata. We then index knowledge indexes that are readily identifiable visually. The proposed model involves two main steps for classifying and recommending metadata.

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“…Breast cancer stem cells have shown the genesis and methods of tumor-resistant strains, and various breast cancer genes have been found. People now have a plethora of pharmaceutical options for breast cancer prevention and treatment, and biological prevention has since been created to provide even greater advantages in terms of living standards [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

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Breast cancer is one of the most dangerous cancers, accounting for a large number of fatalities each year. It is the leading cause of mortality among women globally. It is getting a lot of interest in the scientific community because of its possible life-threatening danger. As a consequence, many machine learning methods (MLMs) have been modified to provide the best results for early diagnosis of this malignancy. Machine learning methods (MLMs) offer several beneficial implications in breast cancer, including early prognosis, detection, and diagnosis. Compared to traditional statistical analysis, machine learning methods (MLMs) have the capacity to improve the analysis of various health data, such as unstructured, complicated, and noisy data. With the demanding prevalence of breast cancer and the arrival of “data reformation,” it is thus imperative to mention the ethical consequences of machine learning (ML) on society and cancer care. It offers conclusively strong tools, smart methods, and efficient algorithms that can help in the prognosis of breast cancer. The focus of this review is on supervised techniques such as classification and regression that may be implemented and used for breast cancer data analysis. Some supervised learning methods like Naive Bayes, AdaBoost, and support vector machine are presented in this work in the early identification of breast cancer. These algorithms have been analyzed for their accuracy and efficiency using various assessment metrics and methods.

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Mining social media text for disaster resource management using a feature selection based on forest optimization

Cited by 19 publications

References 40 publications

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

A Metrics-Based Approach to Metadata Classification Applied in a Recommendation System

A Supervised Learning Identification System for Prognosis of Breast Cancer

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