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 - 5 из 5

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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
;
Ainur Zhakish
;
Nurlan Kulmyrzayev
;
Assylzat Abuova
;
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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Hierarchical Swin Transformer Encoder-Decoder Architecture for Robust Cerebrovascular Abnormality Segmentation in Multimodal MRI
(Science and Information Organization, 2025-01-01)
Nazbek Katayev
;
Zhanel Bakirova
;
Assel N. Kaziyeva
;
Aigerim Bakatkaliyevna Altayeva
;
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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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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Real-Time Road Damage Detection System on Deep Learning Based Image Analysis
(Science and Information Organization, 2024-01-01)
Bakhytzhan Kulambayev
;
Belik Gleb
;
Nazbek Katayev
;
Islam Menglibay
;
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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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
;
Zhanna Yessengaliyeva
;
Zhazira Kozhamkulova
;
Zhanel Bakirova
;
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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