International Journal of Advanced Computer Science and Applications

ISSN

2156-5570

2158-107X

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Science and Information Organization

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Advanced Neural Netwo...

Artificial Intelligen...

Data Mining and Machi...

Network Security and ...

Topic Modeling

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Показано: 1 - 10 из 13

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A Multi-Modal Deep Learning Framework for Student Soft Skills Development in Adaptive Learning Environments
(Science and Information Organization, 2026-01-01)
Marzhan Bekbolat
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Kamalbek Berkimbayev
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Rustam Abdrakhmanov
;
Serik Kenesbayev
;
Nuraim Ibragimova
The rapid evolution of artificial intelligence and adaptive educational technologies has created increasing demand for intelligent systems capable of automatically assessing and enhancing student soft skills within digital learning environments. This study proposes MST-SoftNet, a multimodal transformer-based deep learning framework designed for adaptive soft skill assessment and personalized educational recommendation. The proposed architecture integrates heterogeneous educational modalities, including textual interactions, speech signals, facial expressions, behavioral engagement patterns, performance indicators, and feedback information, within a unified hierarchical transformer fusion framework. Modality-specific encoders, cross-modal attention mechanisms, explainable attention visualization modules, and adaptive recommendation components were incorporated to improve both predictive performance and interpretability. Experimental evaluation was conducted using multiple baseline deep learning architectures, including CNN, LSTM, Transformer, and multimodal CNN-LSTM models. The proposed MST-SoftNet framework achieved superior performance across all evaluation metrics, attaining 93.67% accuracy, 92.11% F1-score, and 96.05% AUC, while simultaneously demonstrating reduced inference latency and improved computational efficiency. Attention visualization analysis further confirmed the capability of the framework to identify semantically meaningful multimodal behavioral patterns associated with communication, collaboration, leadership, creativity, emotional intelligence, and self-regulation competencies. Longitudinal adaptive learning experiments additionally demonstrated substantial improvement in student soft skill progression over time. The obtained results indicate that MST-SoftNet establishes a robust, interpretable, and scalable foundation for next-generation intelligent educational systems focused on personalized soft skill development and adaptive learning optimization.
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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
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Bakһytzһan Omarov
;
Axaule Baibaktina
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Bayan Abilmazhinova
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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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DamageNet: A Dilated Convolution Feature Pyramid Network Mask R‑CNN for Automated Car Damage Detection and Segmentation
(Science and Information Organization, 2025-01-01)
Nazbek Katayev
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Zhanna Yessengaliyeva
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Zhazira Kozhamkulova
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Zhanel Bakirova
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Assylzat Abuova
Automated and precise assessment of vehicle damage is critical for modern insurance processing, accident analysis, and autonomous maintenance systems. In this work, we introduce DamageNet, a unified deep instance segmentation framework that embeds a multi‑rate dilated‑convolution context module within a Feature Pyramid Network (FPN) backbone and couples it with a Region Proposal Network (RPN), RoI‑Align, and parallel heads for classification, bounding‑box regression, and pixel‑level mask prediction. Evaluated on the large‑scale VehiDE dataset comprising 5 200 high‑resolution images annotated for dents, scratches, and broken glass, DamageNet achieves a mean Average Precision (mAP) of 85.7% for damage localization and a mean Intersection over Union (mIoU) of 82.3% for segmentation, outperforming baseline Mask R‑CNN by 6.2 and 7.8 percentage points, respectively. Ablation studies confirm that the dilated‑convolution module, multi‑scale fusion in the FPN, and post‑processing refinements each contribute substantially to segmentation fidelity. Qualitative results demonstrate robust delineation of both subtle scratch lines and extensive panel deformations under diverse lighting and occlusion conditions. Although the integration of atrous convolutions introduces a modest inference overhead, DamageNet offers a significant advancement in end‑to‑end vehicle damage analysis. Future extensions will investigate lightweight dilation approximations, dynamic rate selection, and semi‑supervised learning strategies to further enhance processing speed and generalization to additional damage modalities.
Цитирования (OpenAlex) 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
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Aigerim Bakatkaliyevna Altayeva
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Bayan Abduraimova
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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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An Intelligent Fuzzy-PID Controller for Supporting Comfort Microclimate in Smart Homes
(Science and Information Organization, 2024-01-01)
Nazbek Katayev
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Ainur Zhakish
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Nurlan Kulmyrzayev
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Assylzat Abuova
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Sveta Toxanova
Addressing the challenge of ensuring a comfortable indoor environment in both commercial and residential buildings through the use of heating, ventilation, and air conditioning (HVAC) systems is a critical issue. This challenge is intricately connected to the development of sophisticated multi-channel controllers to regulate temperature and humidity effectively. This academic discussion initially focuses on the development and examination of a complex, interactive, nonlinear mathematical model that encapsulates the ideal parameters for temperature and humidity to achieve the desired comfort levels. The paper then progresses to explore various methodologies in the design of temperature and humidity control systems. It delves into the traditional Proportional-Integral-Derivative (PID) controllers, a mainstay in the industry, and extends to more advanced iterations. These include the integration of PID controllers with distinct decoupled controllers and the innovative combination of PID controllers with self-adjusting parameters, which are informed by the principles of fuzzy logic. This combination is particularly significant for the processes of heating and humidification. Subsequently, the paper presents the results obtained from simulations conducted on a proposed fuzzy-PID controller using Matlab, a widely used computational tool. These simulations are crucial in evaluating the efficacy of the controller design. Additionally, the paper offers an analysis of experimental data collected over a six-month period. This data is instrumental in assessing the real-world performance of the proposed system, providing valuable insights into its practical applicability and effectiveness in managing indoor climate conditions. In summary, this comprehensive study not only lays the groundwork for an interactive model for climate control but also compares various controller designs, culminating in the proposal and evaluation of an advanced fuzzy-PID controller. This work stands as a significant contribution to the ongoing efforts to enhance indoor climate control in buildings.
Цитирования (OpenAlex) 1
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Deep Convolutional Neural Network for Accurate Prediction of Seismic Events
(Science and Information Organization, 2023-01-01)
Assem Turarbek
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Maktagali A. Bektemesov
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А. Д. Онгарбаева
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Assel Orazbayeva
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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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Offensive Language Detection on Social Media using Machine Learning
(Science and Information Organization, 2024-01-01)
Rustam Abdrakhmanov
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Serik Kenesbayev
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Kamalbek Berkimbayev
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Gumyrbek Toikenov
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Elmira Abdrashova
This research paper addresses the critical issue of cyberbullying detection within the realm of social networks, employing a comprehensive examination of various machine learning and deep learning techniques. The study investigates the performance of these methodologies through rigorous evaluation using standard metrics, including Accuracy, Precision, Recall, F-measure, and AUC-ROC. The findings highlight the notable efficacy of deep learning models, particularly the Bidirectional Long Short-Term Memory (BiLSTM) architecture, in consistently outperforming alternative methods across diverse classification tasks. Confusion matrices and graphical representations further elucidate model performance, emphasizing the BiLSTM-based model's remarkable capacity to discern and classify cyberbullying instances accurately. These results underscore the significance of advanced neural network structures in capturing the complexities of online hate speech and offensive content. This research contributes valuable insights toward fostering safer and more inclusive online communities by facilitating early identification and mitigation of cyberbullying. Future investigations may explore hybrid approaches, additional feature integration, or real-time detection systems to further refine and advance the state-of-the-art in addressing this critical societal concern.
Цитирования (OpenAlex) 11Цитирования (OpenCitations) 7
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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
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Аinur Zhumadillayeva
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Sailaugul Avdarsol
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Yermakhan Zhabayev
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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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Hierarchical Swin Transformer Encoder-Decoder Architecture for Robust Cerebrovascular Abnormality Segmentation in Multimodal MRI
(Science and Information Organization, 2025-01-01)
Nazbek Katayev
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Zhanel Bakirova
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Assel N. Kaziyeva
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Aigerim Bakatkaliyevna Altayeva
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Karakat Zhanabaykyzy
This study presents a hierarchical Swin Transformer–based framework for automated segmentation of cerebrovascular structures using multimodal magnetic resonance imaging. The proposed architecture integrates patch partitioning, linear embedding, hierarchical windowed self-attention, and a multilevel encoder–decoder design to address the inherent challenges of vascular segmentation, including irregular morphology, small-caliber vessel visibility, and intensity variability across MRI modalities. A multimodal fusion module enhances the ability to capture complementary anatomical and vascular information, while skip-connected decoding ensures the preservation of fine-grained spatial features essential for accurate vessel reconstruction. The model was evaluated using a combination of open-access datasets and demonstrated superior performance across multiple quantitative metrics, achieving higher Dice similarity, precision, sensitivity, and specificity compared to existing state-of-the-art methods. Qualitative analysis further revealed accurate recovery of major arterial pathways, distal branches, and complex vascular topologies, confirming the model’s robustness in both global and localized segmentation tasks. The results highlight the discriminative strength of hierarchical attention mechanisms and emphasize their role in improving cerebrovascular characterization. Overall, the proposed framework offers a reliable and anatomically coherent approach for vascular segmentation, with strong potential for integration into clinical neuroimaging workflows and advanced cerebrovascular research applications.
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Real-Time Road Damage Detection System on Deep Learning Based Image Analysis
(Science and Information Organization, 2024-01-01)
Bakhytzhan Kulambayev
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Belik Gleb
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Nazbek Katayev
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Islam Menglibay
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Zeinel Momynkulov
This research paper introduces a sophisticated deep learning-based system for real-time detection and segmentation of road damages, utilizing the Mask R-CNN framework to enhance road maintenance and safety. The primary objective was to develop a robust automated system capable of accurately identifying and classifying various types of road damages under diverse environmental conditions. The system employs advanced convolutional neural networks to process and analyze images captured from road surfaces, enabling precise localization and segmentation of damages such as cracks, potholes, and surface wear. Evaluation of the model's performance through metrics like accuracy, precision, recall, and F1-score demonstrated high effectiveness in real-world scenarios. The confusion matrix and loss curves presented in the study illustrate the system's ability to generalize well to unseen data, mitigating overfitting while maintaining high detection sensitivity. Challenges such as variable lighting, shadows, and background noise were addressed, highlighting the system's resilience and the need for further dataset diversification and integration of multimodal data sources. The potential improvements discussed include refining the convolutional network architecture and incorporating predictive maintenance capabilities. The system's application extends beyond mere detection, promising transformative impacts on urban planning and infrastructure management by integrating with smart city frameworks to facilitate real-time, predictive road maintenance. This research sets a benchmark for future developments in the field of automated road assessment, pointing towards a future where AI-driven technologies significantly enhance public safety and infrastructure efficiency.
Цитирования (OpenAlex) 9Цитирования (OpenCitations) 7

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