Offensive Language Detection using Artificial Neural Network

Susanty, Meredita; Sahrul,; Rahman, Ahmad Fauzan; Normansyah, Muhammad Dzaky; Irawan, Ade

doi:10.1109/icaiit.2019.8834452

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…For text classification using the machine-learning approach, researchers have used several models to classify whether a text contain hate speech and abusive language or not including Naive Bayes (NB) [5] , [44] , [1] , [20] , [40] , [4] , [24] , [25] , [38] , Support Vector Machine (SVM) [5] , [34] , [44] , [1] , [20] , [40] , [7] , [24] , [25] , [38] , [27] , Logistic Regression (LR) [5] , [39] , [44] , [40] , [7] , [27] , Decision Tree (DT) [44] , Random Forest Decision Tree (RFDT) [5] , [39] , [1] , [20] , [7] , [24] , [25] , [38] , [27] , k-Nearest Neighbor (kNN) [34] , [44] , Latent Semantic Analysis (LSA) [3] , Maximum Entropy [20] , [19] , and Artificial Neural Network (ANN) [49] . These machine-learning models are usually combined with several text features including word n-grams [5] , [39] , [1] , [40] , [7] , [49] , [4] , [24] , [25] , [38] , [27] , character n-grams [5] , [39] , [1] , [40] , ...…”

Section: Methodsmentioning

confidence: 99%

Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Ibrohim¹,

Budi²

2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Ibrohim¹,

Budi²

2023

Heliyon

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“…Artificial neural network models are influenced by the human brain structure, which interconnects many biological neurons that are important for maintaining coherent communication. The ANN architecture is computer-based and consists of a variety of simple parallel processing units ( 49 ). It is a common statistical technique that can analyze exact relationships between variables.…”

Section: Proposed Methodsmentioning

confidence: 99%

Machine Learning Assisted Cervical Cancer Detection

Mehmood

Rizwan

ml.

et al. 2021

Front. Public Health

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Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named CervDetect that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. CervDetect uses Pearson correlation between input variables as well as with the output variable to pre-process the data. CervDetect uses the random forest (RF) feature selection technique to select significant features. Finally, CervDetect uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that CervDetect accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100%.

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“…ANN was developed to recognize and influence the functional features and mathematical characteristics of the brain as it executes cognitive functions, including sensorial interpretation, object categorization, concept association, and learning. Today, though, a lot of research is put into designing neural networks for applications like pattern recognition and classification, data compression, and optimization [27]. Python is one of ANN's most important programs.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

Chicho

Sallow

2021

JSCDM

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Python is one of the most widely adopted programming languages, having replaced a number of those in the field. Python is popular with developers for a variety of reasons, one of which is because it has an incredibly diverse collection of libraries that users can run. The most compelling reasons for adopting Keras come from its guiding principles, particularly those related to usability. Aside from the simplicity of learning and model construction, Keras has a wide variety of production deployment options and robust support for multiple GPUs and distributed training. A strong and easy-to-use free, open-source Python library is the most important tool for developing and evaluating deep learning models. The aim of this paper is to provide the most current survey of Keras in different aspects, which is a Python-based deep learning Application Programming Interface (API) that runs on top of the machine learning framework, TensorFlow. The mentioned library is used in conjunction with TensorFlow, PyTorch, CODEEPNEATM, and Pygame to allow integration of deep learning models such as cardiovascular disease diagnostics, graph neural networks, identifying health issues, COVID-19 recognition, skin tumors, image detection, and so on, in the applied area. Furthermore, the author used Keras's details, goals, challenges, significant outcomes, and the findings obtained using this method.

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Offensive Language Detection using Artificial Neural Network

Cited by 14 publications

References 6 publications

Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Machine Learning Assisted Cervical Cancer Detection

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

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