Hybrid System for MPAA Ratings of Movie Clips Using Support Vector Machine

Vishwakarma, Gagan; Thakur, Ghanshyam Singh

doi:10.1007/978-981-13-1595-4_45

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…True positive (TP) represents the correctly identified web pages, false positive (FP) represents the incorrectly identified web pages, true negative (TN) represents the correctly rejected web pages, and false-negative (FN) represents the incorrectly rejected web pages. Precision denoted as (P) shows the correctness of the proposed model calculated using Equation (11),…”

Section: Performance Matricesmentioning

confidence: 99%

“…The Support Vector Machine is a supervised machine learning model based on structural risk minimization and was introduced by Vapnik [11,42]. SVM creates a hyperplane that separates the data into two sets with the maximum margin.…”

Section: • Svmmentioning

confidence: 99%

“…Researchers have used the Natural Language Processing (NLP) word embedding technique and the Long Short Term Memory (LSTM) layer in recent advances in deep learning [8]. The application domains of stated techniques include text classification, chatbots, sentiment analysis, object classification, and sequence problems [9][10][11]. The word embedding technique has been frequently utilized in NLP to produce feature vectors that are semantically correlated.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Features with Contextual Knowledge-Based Web Page Categorization Using the GloVe Model and Stacked BiLSTM

Nandanwar

Choudhary

2021

Symmetry

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Internet technologies are emerging very fast nowadays, due to which web pages are generated exponentially. Web page categorization is required for searching and exploring relevant web pages based on users’ queries and is a tedious task. The majority of web page categorization techniques ignore semantic features and the contextual knowledge of the web page. This paper proposes a web page categorization method that categorizes web pages based on semantic features and contextual knowledge. Initially, the GloVe model is applied to capture the semantic features of the web pages. Thereafter, a Stacked Bidirectional long short-term memory (BiLSTM) with symmetric structure is applied to extract the contextual and latent symmetry information from the semantic features for web page categorization. The performance of the proposed model has been evaluated on the publicly available WebKB dataset. The proposed model shows superiority over the existing state-of-the-art machine learning and deep learning methods.

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Section: Performance Matricesmentioning

confidence: 99%

Section: • Svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic Features with Contextual Knowledge-Based Web Page Categorization Using the GloVe Model and Stacked BiLSTM

Nandanwar

Choudhary

2021

Symmetry

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“…In this context, the development of effective nudity classification systems has become essential for content moderation in online platforms to ensure user safety and well-being. While traditional machine learning models [4] [5] and convolutional neural networks (CNNs) [6] [7] [8] [9], have been widely used for nudity classification, recent transformer-based models [10] and modern CNN architectures [11] have shown promising results in image classification tasks.…”

Section: Introductionmentioning

confidence: 99%

State-of-the-Art in Nudity Classification: A Comparative Analysis

Akyon

Temizel

2023

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

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This paper presents a comparative analysis of existing nudity classification techniques for classifying images based on the presence of nudity, with a focus on their application in content moderation. The evaluation focuses on CNN-based models, vision transformer, and popular opensource safety checkers from Stable Diffusion and Largescale Artificial Intelligence Open Network (LAION). The study identifies the limitations of current evaluation datasets and highlights the need for more diverse and challenging datasets. The paper discusses the potential implications of these findings for developing more accurate and effective image classification systems on online platforms. Overall, the study emphasizes the importance of continually improving image classification models to ensure the safety and well-being of platform users. The project page, including the demonstrations and results is publicly available at https://github.com/fcakyon/content-moderation-deep-learning.

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