International Journal of Advanced Computer Science and Applications

ISSN

2156-5570

2158-107X

Издатель

Science and Information Organization

Ключевые слова

Advanced Neural Netwo...

Artificial Intelligen...

Data Mining and Machi...

Network Security and ...

Topic Modeling

Публикации в журнале

Показано: 1 - 3 из 3

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Automatic Classification of Scanned Electronic University Documents using Deep Neural Networks with Conv2D Layers
(Science and Information Organization, 2023-01-01)
Aigerim S. Baimakhanova
;
Аinur Zhumadillayeva
;
Sailaugul Avdarsol
;
Yermakhan Zhabayev
;
Makhabbat Revshenova
This paper proposes a novel approach for scanned document categorization using a deep neural network architecture. The proposed approach leverages the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract features from the scanned documents and model the dependencies between words in the documents. The pre-processed documents are first fed into a CNN, which learns and extracts features from the images. The extracted features are then passed through an RNN, which models the sequential nature of the text. The RNN produces a probability distribution over the predefined categories, and the document is classified into the category with the highest probability. The proposed approach is evaluated on a dataset of scanned documents, where each document is categorized into one of four predefined categories. The experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods. The proposed approach achieves an overall accuracy of 97.3%, which is significantly higher than the existing methods' accuracy. Additionally, the proposed approach's performance was robust to variations in the quality of the scanned documents and the OCR accuracy. The contributions of this paper are twofold. Firstly, it proposes a novel approach for scanned document categorization using deep neural networks that leverages the strengths of CNNs and RNNs. Secondly; it demonstrates the effectiveness of the proposed approach on a dataset of scanned documents, highlighting its potential applications in various domains, such as information retrieval, data mining, and document management. The proposed approach can help organizations manage and analyze large volumes of data efficiently.
Цитирования (OpenAlex) 2Цитирования (OpenCitations) 1
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A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities
(Science and Information Organization, 2023-01-01)
Bakһytzһan Omarov
;
N.K. Nurmash
;
Bauyrzhan Doskarayev
;
Nagashbek Zhilisbaev
;
Maxat Dairabayev
In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.
Цитирования (OpenAlex) 24Цитирования (OpenCitations) 16
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Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data
(Science and Information Organization, 2024-01-01)
Altynzer Baiganova
;
Saniya Toxanova
;
Meruyert Yerekesheva
;
НАУРЫЗОВА Н.К.
;
Zhanar Zhumagalieva
With the burgeoning use of social media platforms, online harassment and cyberbullying have become significant concerns. Traditional mechanisms often falter, necessitating advanced methodologies for efficient detection. This study presents an innovative approach to identifying cyberbullying incidents on social media sites, employing a hybrid neural network architecture that amalgamates Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). By harnessing the sequential processing capabilities of LSTM to analyze the temporal progression of textual data, and the spatial discernment of CNN to pinpoint bullying keywords and patterns, the model demonstrates substantial improvement in detection accuracy compared to extant methods. A diverse dataset, encompassing multiple social media platforms and linguistic styles, was utilized to train and test the model, ensuring robustness. Results evince that the LSTM-CNN amalgamation can adeptly handle varied sentence structures and contextual nuances, outstripping traditional machine learning classifiers in both specificity and sensitivity. This research underscores the potential of hybrid neural networks in addressing contemporary digital challenges, urging further exploration into blended architectures for nuanced problem-solving in cyber realms.
Цитирования (OpenAlex) 4

International Journal of Advanced Computer Science and Applications

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