Analytics of machine learning-based algorithms for text classification

Hassan, Sayar Ul; Ahamed, Jameel; Ahmad, Khaleel

doi:10.1016/j.susoc.2022.03.001

Cited by 60 publications

(27 citation statements)

References 27 publications

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“…Matheus claims that classification algorithms usually generate a discrete or binary output. The machine learning classifiers comprise random forest, logistic regression, and support vector machines (Hassan et al, 2022). In unsupervised learning, patterns and associations are inferred from input data that does not have known labels.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter

Alharbi,

Khafaga,

Zaki

et al. 2024

Front. Energy Res.

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Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter

Alharbi,

Khafaga,

Zaki

et al. 2024

Front. Energy Res.

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“…Feature engineering might be refined for better outcomes, and deep learning could be employed for classification tasks. [5] The evaluation article of "Analytics of machine learningbased algorithms for text classification" shows The performance of several machine learning algorithms on different datasets is studied and compared [3].According to the paper "A Survey on Computational Propaganda Detection," this new form of self-expression has had unintended consequences, the most evident of which is that society is now open to future threats emanating from a number of sources. The topic drew a lot of attention in 2016, a year marked by unprecedented levels of micro targeted online disinformation and misinformation, particularly in the aftermath of Brexit and the 2016 presidential election.…”

Section: Related Workmentioning

confidence: 99%

Detection of Textual Propaganda Using Passive Aggressive Classifiers

2023

IJATCSE

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Nowadays, social media activity, particularly news that spreads over the network, is a major source of knowledge. People search out and chew up news from internet-based living because of the low effort, easy access, and rapid dissemination of information. Twitter, as one of the most wellknown continuing news sources, also happens to be one of the most dominant news disseminating media. It has already been known to wreak significant harm by disseminating snippets of gossip. Online clients are typically susceptible, and everything they do on web-based networking media is assumed to be trustworthy. As a result, automating counterfeit propaganda detection is critical to maintaining a vibrant online media and informal organization. In order to computerize propaganda news identification in Twitter datasets, this research develops a technique for recognizing propaganda text messages from tweets by figuring out how to anticipate precision evaluations. This paper proposes a supervised machine learning technique, Passive aggressive classifiers that uses Count Vectorizer and Term FrequencyInverse Document Frequency Vectorizer as feature extraction to detect propaganda news based on the polarity of the corresponding article. Finally, this algorithm uses dataset with 43000 records and shows good accuracy.

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“…6) Stopwords: Stopwords are commonly used in a text mining project with little influence [23]. "The", "A", "Is," and "Are" are stop words.…”

Section: ) Data Acquisitionmentioning

confidence: 99%

An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence

Ismail¹,

Yusoff²

2022

IJACSA

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Gender-based violence is a public health issue that needs high concern to eliminate discrimination and violence against women and girls. Several cases are through the offline organization and the respective online platform. However, many victims share their experiences and stories on social media platforms. Twitter is one of the methods for locating and identifying gender-based violence based on its type. This paper proposed a hybrid Long Short-Term Memory (LSTM) and Convolution Neural Network CNN with GloVe to perform multiclassification of gender violence. Intimate partner violence, harassment, rape, femicide, sex trafficking, forced marriage, forced abortion, and online violence against women are e eight gender violence keyword for data extraction from Twitter text data. Next is data cleaning to remove unnecessary information. Normalization converts data into a structure the machine can recognize as model input. The evaluation considers cross-entropy loss parameters, learning rate, an optimizer, and epochs. LSTM+GloVe vector embedding outperforms all other methods. CNN-LSTM+Glove and LSTM-CNN+GloVe achieved 0.98 for test accuracy, 0.95 for precision, 0.94 for recall, and 0.95 for the f1-score. The findings can help the public and relevant agencies differentiate and categorize different types of gender violence through text. With this effort, the government can use as one of the mechanisms that indirectly can support monitoring of the current situation of gender violence.

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Analytics of machine learning-based algorithms for text classification

Cited by 60 publications

References 27 publications

Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter

Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter

Detection of Textual Propaganda Using Passive Aggressive Classifiers

An Efficient Hybrid LSTM-CNN and CNN-LSTM with GloVe for Text Multi-class Sentiment Classification in Gender Violence

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