The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning

Mohamed, Abdel‐Baset A.; Amer, Eslam; Eldin, sara Noor; khaled, jana; Hossam, Maysoon; ElMasry, Noha; Adnan, Ganna Tamer

doi:10.21608/jocc.2022.218453

Cited by 32 publications

(3 citation statements)

References 40 publications

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“…Spam spread, phishing, malware dissemination, and cyberbullying are all examples of aggressive conduct. Due to the fact that social networking sites provide users the freedom to publish on their services, textual cyberbullying has been the most common hostile conduct [26][27][28].…”

Section: Related Workmentioning

confidence: 99%

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Sultan¹,

Toktarova²,

Zhumadillayeva³

et al. 2023

Computers, Materials &Amp; Continua

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Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying. However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem. In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem. We deployed three deep and six shallow learning algorithms for cyberbullying detection problems. The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.

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Section: Related Workmentioning

confidence: 99%

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Sultan¹,

Toktarova²,

Zhumadillayeva³

et al. 2023

Computers, Materials &Amp; Continua

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“…The attribute "diagnosis" has been denoted as the class label that classifies the tumor as Malignant (M) and Benign (B). In the literature, the majority of the papers worked on unstructured data for Breast cancer like mammograms [42], [43]. • Z-Alizadehsani Dataset: The data was collected from heart disease patients at Shaheed Rajaei Cardiovascu- lar, Medical, and Research Center, Tehran, Iran.…”

Section: A Datasetsmentioning

confidence: 99%

Particle Swarm Optimization-Based Random Forest Framework for the Classification of Chronic Diseases

Singh,

Prakash,

Jain

2023

IEEE Access

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In this paper, a hybrid metaheuristic-based Machine learning approach has been propounded for the classification of various Chronic Diseases (CDs). The CDs often get misdiagnosed due to various issues viz., similar and overlapping symptoms, sensitive devices, lack of clinical experts, etc. Based on the above issues, this study has utilized a fusion of Particle Swarm Optimization with Random Forest (PSORF) for the automatic identification of CDs. The approach PSORF comprises of two main components: PSO for obtaining the minimal optimal feature set, also to optimize the performance of the RF classifier, and RF classifier for the classification of multiple CDs. In this research, five different CD datasets have been deployed onto a series of experiments have been conducted to identify the best approach for the classification of CDs. To address the issues of imbalanced and incomplete data in the datasets used, Synthetic Minority Oversampling Technique (SMOTE) and Expected Minimization (EM) Imputation techniques have been applied before training the model. This ensures the data quality is improved before being used for analysis. Furthermore, the performance of the PSO and RF classifiers has been compared with other metaheuristic and ML classifiers in terms of different performance metrics. For this purpose, Friedman's tests have been employed to calculate the mean ranks of all the classifiers across all the datasets for different metrics. The results showed that the proposed technique achieved the highest mean rank in terms of Accuracy, F-measure, and Receiver Operating Characteristics (ROC) across all four datasets.

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“…The use of pre-trained language models has also been explored for hate speech detection. Like [28] proposed a BERT-based model for detecting hate speech on social media platforms, demonstrating superior performance compared to traditional machine learning techniques. Similarly, [29] employed BERT for detecting hate speech on Twitter, highlighting the model's ability to capture the complex semantics of text and adapt to various linguistic contexts.…”

Section: Hybrid Approaches and Ensemble Modelsmentioning

confidence: 99%

Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Toktarova,

Syrlybay,

Myrzakhmetova

et al. 2023

IJACSA

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Hate speech on social media platforms like Twitter is a growing concern that poses challenges to maintaining a healthy online environment and fostering constructive communication. Effective detection and monitoring of hate speech are crucial for mitigating its adverse impact on individuals and communities. In this paper, we propose a comprehensive approach for hate speech detection on Twitter using both traditional machine learning and deep learning techniques. Our research encompasses a thorough comparison of these techniques to determine their effectiveness in identifying hate speech on Twitter. We construct a robust dataset, gathered from diverse sources and annotated by experts, to ensure the reliability of our models. The dataset consists of tweets labeled as hate speech, offensive language, or neutral, providing a more nuanced representation of online discourse. We evaluate the performance of LSTM, BiLSTM, and CNN models against traditional shallow learning methods to establish a baseline for comparison. Our findings reveal that deep learning techniques outperform shallow learning methods, with BiLSTM emerging as the most accurate model for hate speech detection. The BiLSTM model demonstrates improved sensitivity to context, semantic nuances, and sequential patterns in tweets, making it adept at capturing the intricate nature of hate speech. Furthermore, we explore the integration of word embeddings, such as Word2Vec and GloVe, to enhance the performance of our models. The incorporation of these embeddings significantly improves the models' ability to discern between hate speech and other forms of online communication. This paper presents a comprehensive analysis of various machine learning methods for hate speech detection on Twitter, ultimately demonstrating the superiority of deep learning techniques, particularly BiLSTM, in addressing this critical issue. Our findings pave the way for further research into advanced methods of tackling hate speech and facilitating healthier online interactions.

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The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning

Cited by 32 publications

References 40 publications

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Particle Swarm Optimization-Based Random Forest Framework for the Classification of Chronic Diseases

Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

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