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 - 6 из 6

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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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Development of a Framework for Classification of Impulsive Urban Sounds using BiLSTM Network
(Science and Information Organization, 2023-01-01)
Nazbek Katayev
;
Aigerim Bakatkaliyevna Altayeva
;
Bayan Abduraimova
;
Nurgul Kurmanbekkyzy
;
Zh. Madibaiuly
Urban environments are awash with myriad sounds, among which impulsive noises stand distinct due to their brief and often disruptive nature. As cities evolve and expand, the accurate classification and management of these impulsive sounds become paramount for urban planners, environmental scientists, and public health advocates. This paper introduces a novel framework leveraging the Bidirectional Long Short-Term Memory (BiLSTM) Network for the systematic categorization of impulsive urban sounds. Traditional methodologies often falter in recognizing the nuanced intricacies of such noises. In contrast, the presented BiLSTM-based approach adapts to the temporal variability intrinsic to these sounds, thereby enhancing classification accuracy. The research harnesses an expansive dataset, curated from various urban settings, to train and validate the model. Preliminary findings suggest that our BiLSTM framework outperforms existing models, with a marked increase in both specificity and sensitivity metrics. The outcome of this study holds profound implications for city acoustics management, noise pollution control, and urban health interventions. Moreover, the framework's adaptability paves the way for its application across diverse acoustic landscapes beyond the urban realm. Future endeavors should seek to further optimize the model by integrating more diverse soundscapes and addressing potential biases in data collection.
Цитирования (OpenAlex) 3Цитирования (OpenCitations) 3
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Deep Convolutional Neural Network for Accurate Prediction of Seismic Events
(Science and Information Organization, 2023-01-01)
Assem Turarbek
;
Maktagali A. Bektemesov
;
А. Д. Онгарбаева
;
Assel Orazbayeva
;
Aizhan Koishybekova
In recent years, the realm of seismology has witnessed an increased integration of advanced computational techniques, seeking to enhance the precision and timeliness of earthquake predictions. The paper titled "Deep Convolutional Neural Network and Machine Learning Enabled Framework for Analysis and Prediction of Seismic Events" embarks on an ambitious exploration of this interstice, marrying the formidable prowess of Deep Convolutional Neural Networks (CNNs) with an array of machine learning algorithms. At the forefront of our investigation is the Deep CNN, known for its unparalleled capability to process spatial hierarchies and multi-dimensional seismic data. Accompanying this neural behemoth is LightGBM, a gradient boosting framework that offers superior speed and performance, especially with voluminous datasets. Additionally, conventional neural networks, noted for their adeptness in pattern recognition, offer a robust method to gauge the intricacies of seismic data. Our exploration doesn't halt here; the research delves deeper with Random Forest and Support Vector Machines (SVM), both renowned for their resilient performance in classification tasks. By amalgamating these diverse methodologies, this research crafts a multifaceted and synergistic framework. The culmination is a sophisticated tool poised to not only discern the minutiae of seismic activities with heightened accuracy but to predict forthcoming events with a degree of certainty previously deemed elusive. In this era of escalating seismic activities, our research offers a timely beacon, heralding a future where communities are better equipped to respond to the Earth's capricious tremors.
Цитирования (OpenAlex) 3Цитирования (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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Applying Artificial Intelligence and Computer Vision for Augmented Reality Game Development in Sports
(Science and Information Organization, 2023-01-01)
Nurlan Omarov
;
Bakһytzһan Omarov
;
Axaule Baibaktina
;
Bayan Abilmazhinova
;
Tolep Abdimukhan
This paper delineates the intricate process of crafting an Augmented Reality (AR)-enriched version of the Subway Surfers game, engineered with an emphasis on action recognition and the leverage of Artificial Intelligence (AI) principles, with the primary objective of boosting children's enthusiasm towards physical activity. The gameplay, fundamentally predicated on advanced computer vision methodologies for discerning player kinesthetics, and reinforced with machine learning tactics for modulating the intricacy of the game in accordance with player capabilities, offers an immersive and engaging interface. This innovative amalgamation serves to not only catalyze children's interest in participating in active exercises, but also introduces a playful aspect to it. The procedural development of the game required the cohesive assimilation of a diverse spectrum of technologies, encompassing Unity for game development, TensorFlow for implementing machine learning algorithms, and Vuforia for crafting the AR elements. A preliminary study, conducted to assess the efficacy of the game in fostering a pro-sport attitude in children, reported encouraging outcomes. Given the potential of the game to incite physical activity among young users, it could be construed as a promising antidote to sedentarism and a potent catalyst for endorsing a healthier lifestyle.
Цитирования (OpenAlex) 7Цитирования (OpenCitations) 3
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Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports
(Science and Information Organization, 2023-01-01)
Bauyrzhan Doskarayev
;
Nurlan Omarov
;
Bakһytzһan Omarov
;
Zhuldyz Ismagulova
;
Zhadra Kozhamkulova
This research paper presents the development of computer vision-enabled augmented reality games based on action detection to increase motivation for sports. With the increasing popularity of digital games, physical activity and sports participation have been declining, especially among the younger generation. To address this issue, we developed a series of augmented reality games that require players to perform physical actions to progress and succeed in the game. These games were developed using computer vision technology to detect the players' movements and provide real-time feedback, enhancing the gaming experience and promoting physical activity. The results of our user study showed that participants who played the augmented reality games reported higher levels of motivation to engage in physical activity and sports. The findings suggest that computer vision-enabled augmented reality games can be an effective tool to promote physical activity and sports participation, especially among younger generations.
Цитирования (OpenAlex) 15Цитирования (OpenCitations) 10

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