A hybrid model combining neural networks and decision tree for comprehension detection

The Artificial Neural Network is generally considered to be an effective classifier, but also a “Black Box” component whose internal behavior cannot be understood by human users. This lack of transparency forms a barrier to acceptance in high-stakes applications by the general public. This paper investigates the use of a hybrid model comprising multiple artificial neural networks with a final C4.5 decision tree classifier to investigate the potential of explaining the classification decision through production rules. Two large datasets collected from comprehension studies are used to investigate the value of the C4.5 decision tree as the overall comprehension classifier in terms of accuracy and decision transparency. Empirical trials show that higher accuracies are achieved through using a decision tree classifier, but the significant tree size questions the rule transparency to a human

Text Classification: A Review, Empirical, and Experimental Evaluation

The explosive and widespread growth of data necessitates the use of text classification to extract crucial information from vast amounts of data. Consequently, there has been a surge of research in both classical and deep learning text classification methods. Despite the numerous methods proposed in the literature, there is still a pressing need for a comprehensive and up-to-date survey. Existing survey papers categorize algorithms for text classification into broad classes, which can lead to the misclassification of unrelated algorithms and incorrect assessments of their qualities and behaviors using the same metrics. To address these limitations, our paper introduces a novel methodological taxonomy that classifies algorithms hierarchically into fine-grained classes and specific techniques. The taxonomy includes methodology categories, methodology techniques, and methodology sub-techniques. Our study is the first survey to utilize this methodological taxonomy for classifying algorithms for text classification. Furthermore, our study also conducts empirical evaluation and experimental comparisons and rankings of different algorithms that employ the same specific sub-technique, different sub-techniques within the same technique, different techniques within the same category, and categorie

Enhancing Intrusion Detection Systems with a Hybrid Deep Learning Model and Optimized Feature Composition

Systems for detecting intrusions (IDS) are essential for protecting network infrastructures from hostile activity. Advanced methods are required since traditional IDS techniques frequently fail to properly identify sophisticated and developing assaults. In this article, we suggest a novel method for improving IDS performance through the use of a hybrid deep learning model and feature composition optimization. RNN and CNN has strengths that the proposed hybrid deep learning model leverages to efficiently capture both spatial and temporal correlations in network traffic data. The model can extract useful features from unprocessed network packets using CNNs and RNNs, giving a thorough picture of network behaviour. To increase the IDS's ability to discriminate, we also offer feature optimization strategies. We uncover the most pertinent and instructive features that support precise intrusion detection through a methodical feature selection and engineering process. In order to reduce the computational load and improve the model's efficiency without compromising detection accuracy, we also use dimensionality reduction approaches. We carried out extensive experiments using a benchmark dataset that is frequently utilized in intrusion detection research to assess the suggested approach. The outcomes show that the hybrid deep learning model performs better than conventional IDS methods, obtaining noticeably greater detection rates and lower false positive rates. The performance of model is further improved by the optimized feature composition, which offers a more accurate depiction of network traffic patterns

Review Paper on Enhancing COVID-19 Fake News Detection With Transformer Model

The growing propagation of disinformation about the COVID-19 epidemic needs powerful fake news detection technologies. This review provides an in-depth examination of existing techniques, including traditional machine learning methods such as Random Forest and Naive Bayes, as well as sophisticated models for deep learning such as Bi- GRU, CNN, and LSTM, RNN, & transformer-based architecture such as BERT and XLM- Roberta, are also available. One noticeable development is the merging of traditional algorithmswith sophisticated transformers, which emphasize the quest of improved accuracy and flexibility.However, important research gaps have been identified. There has been little research on cross- lingual detection algorithms, revealing a substantial gap in multilingual false news detection, which is critical in the global context of COVID-19 information spread. Furthermore, the researchemphasizes the need of flexible methodologies by emphasizing the need for appropriate preprocessing strategies for various content types. Furthermore, the lack of common assessment measures is a barrier, underlining the need of unified frameworks for successfully benchmarking and comparing models. This analysis provides light on the changing COVID-19 false news detection environment, emphasizing the need for novel, adaptive, and internationally relevant approaches to successfully address the ubiquitous dissemination of disinformation during the current pandemic