Scientific Journal of Astana IT University
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COMBINED APPROACH BASED ON HARALICK AND GABOR FEATURES TO CLASSIFY BUILDINGS PARTIALLY HIDDEN BY VEGETATION
Get PDFClassification of urban area is important for urban planning, infrastructure management and detection of illegal constructions. However, automatic object recognition in urban environments is difficult due to textural similarity of materials, varying lighting conditions and partial overlap of buildings with vegetation. The identification of buildings partially hidden by green spaces is particularly challenging because their boundaries merge with the surrounding environment, which reduces the accuracy of traditional classification methods. In this study, a stepwise approach to object classification in aerial images is proposed to improve the recognition of buildings partially hidden by vegetation. The analysis was performed in two stages using three-channel high-resolution aerial images acquired from an unmanned aerial vehicle. In the first stage, classification was performed based on Haralick features computed from a co-occurrence matrix of gradations, which allowed the extraction of statistical texture features. However, this was insufficient for accuracy, so in the second stage, a Gabor filter was additionally applied to provide analysis of local texture features, taking into account the frequency and orientation of image elements. The final classification was performed using Random Forest algorithm, which allowed to divide objects into three categories: "buildings", "vegetation" and "buildings partially hidden by vegetation". The classes "buildings" and "vegetation" were considered as auxiliary, providing quality control of the classification and allowing us to focus on improving the recognition of objects partially occluded by vegetation. Experimental results confirmed that the proposed method is effective for recognizing buildings partially hidden by vegetation. The inclusion of the Gabor filter improved the classification accuracy of this class from 0.84 to 0.90, the completeness from 0.74 to 0.86, and the F1-estimation from 0.79 to 0.88. The 11% improvement in completeness is particularly important because it indicates a reduction in the number of missed buildings. In comparison, the classification accuracy of fully visible buildings increased from 0.84 to 0.91 and that of vegetation from 0.88 to 0.95. Thus, the proposed method, which combines global and local texture features, demonstrated high performance to improve the identification accuracy of complex objects whose boundaries merge with the surrounding vegetation
MULTI-OUTPUT BUS TRAVEL TIME PREDICTION USING CONVOLUTIONAL LSTM NEURAL NETWORKS
Get PDFEnsuring accurate and dependable predictions of bus arrival times is essential to improving public transportation services and maintaining their appeal in urban settings. Such predictions, whether displayed on electronic boards or integrated into mobile applications, enable passengers to make better travel decisions, such as choosing alternate routes, anticipating delays, or avoiding missed connections. Furthermore, advanced Intelligent Transport Systems (ITS) utilize this information to facilitate smoother passenger transfers by holding delayed services within predefined limits. However, as urban congestion and travel time unpredictability grow, traditional methods face significant challenges in providing reliable predictions, making the problem increasingly complex. This research focuses on developing a robust system for forecasting bus arrival times in Astana city, utilizing extensive spatio-temporal data from two datasets. Multiple machine learning and deep learning models are implemented and compared to achieve this goal. These include K-means clustering to classify bus routes, K-Nearest Neighbors (KNN) for predictions based on proximity, and a Conv-LSTM model, which integrates convolutional and long short-term memory layers to address intricate temporal and spatial correlations. Support Vector Machines (SVM) and regression models are also incorporated to establish benchmarks and comparative insights. Through empirical evaluation, the proposed models demonstrate varying strengths, with the Conv-LSTM model showing exceptional performance in adapting to dynamic urban conditions and detecting subtle fluctuations in bus travel times. The findings highlight the transformative potential of sophisticated predictive modeling techniques to enhance urban transit systems, ensuring passengers receive timely and accurate information while improving overall operational efficiency.
ASSESSMENT OF UNPROFITABILITY OF COMPULSORY EMPLOYEE ACCIDENT INSURANCE TARIFFS IN KAZAKHSTAN
Get PDFCompulsory insurance of employees against industrial accidents is an important social protection tool in Kazakhstan, but the current rates are insufficiently stable and do not cover the actual risks, which leads to financial imbalances among insurers and reduces the level of compensation for employees. The purpose of this study is to identify the reasons for the unprofitability of existing rates and to develop proposals for their modernization based on modern risk assessment methods. The study used actuarial calculations, including analysis of loss ratios, payments, and total expenses, as well as the construction of linear trends to forecast future losses. In addition, statistical modeling methods were used, including probability distributions and Markov chains, which made it possible to justify the introduction of a differentiated tariff system and a bonus-malus mechanism for enterprises depending on the level of industrial injuries. The results of the analysis showed an increase in the loss ratio from 11.87 percent in 2020 to 35.33 percent in the second quarter of 2024 and an increase in the aggregate loss ratio from 65.6 to 73.1 percent. The calculations determined a new base rate of 0.6842 percent compared to the current level of 0.59 percent, reflecting the need to revise insurance rates to ensure the financial stability of the system. A two-tier tariff model has been proposed, taking into account industry and occupational risks, as well as a bonus-malus mechanism that creates economic incentives for employers to invest in improving occupational safety and reducing the number of accidents. The practical significance of the study lies in the possibility of applying the results obtained by insurers, government agencies, and employers in forming a balanced and sustainable compulsory insurance system capable of simultaneously strengthening the financial stability of the insurance market and increasing the social protection of workers
CLASSIFICATION OF HUMAN EMOTIONS USING THERMOGRAMS AND NEURAL NETWORK
Get PDFAs information systems and technologies continue to evolve, there remains a noticeable gap in the efficiency and practicality of data processing algorithms, especially in the field of emotion recognition. This study explores several neural network models designed to classify emotions based on thermal images (thermograms). The dataset used for training included 1,642 images, some of which were generated through augmentation, with all images captured while participants viewed emotionally charged videos. The goal was to recognize six basic emotions: joy, sadness, fear, disgust, anger, and surprise. To identify the most effective architecture, the performance of five models were compared: a standard convolutional neural network (CNN), Quadruplet Network, U-Net, Inception, and SqueezeNet. Each model was trained on the same dataset under consistent conditions. Classification accuracy and validation loss were the main evaluation metrics. In addition, data augmentation and early stopping were applied to improve generalization and prevent overfitting. Among the tested architectures, the Inception model achieved the highest test accuracy of 97.5%, while the Quadruplet Network achieved 96.85% accuracy with a lower validation loss of 0.571, indicating stronger generalization. These results suggest that both models are well-suited for real-time emotion recognition using thermal imaging. The findings highlight the potential of combining infrared data with modern neural architectures to advance emotion detection systems beyond traditional RGB-based methods
EVALUATING AN ANALYTICAL MODEL OF CYBERATTACK EFFECTS ON AN IIoT SYSTEM WITH EDGE COMPUTING CAPABILITIES
Get PDFThe Industrial Internet of Things (IIoT) is an important component of future industrial systems. Implementing edge computing in the IIoT can significantly reduce decision latency, save bandwidth resources, and protect privacy to some extent. But it is important to realize that edge computing is often resource-constrained, and devices are often spread across vast geographic areas, including intermittent network connectivity. Such conditions increase security vulnerabilities due to increased attack surfaces and physical availability. This paper addresses the problem of securing IIoT systems utilizing the concept of edge computing. An analytical model of attack influences is proposed, including typical scenarios and individual steps of attacks, both physical and software-informational in nature. The presented analytical model is designed to assess and analyze attack impacts on IIoT, implements the concept of boundary calculations, allows to analyze vulnerabilities of IIoT systems more effectively and develop measures to protect them. The model is designed to provide a comprehensive tool for securing critical infrastructures. The model includes typical attack scenarios, detailed attack steps, and impact classification. The developed model can be used for risk analysis, development of protection strategies, and security testing of IIoT systems. The conducted experimental study confirmed the relevance and practical significance of the developed model. The results of the study showed that IIoT-systems using edge computing are subject to a wide range of threats. The most critical are DoS attacks and Data Integrity Attacks. The obtained results emphasize the need to apply comprehensive security measures for IIoT systems with edge computing and confirm the effectiveness of the proposed analytical model
DEVELOPMENT OF THE INTEGRATED WATER RESOURCES MONITORING AND FORECASTING MODULE FOR DECISION SUPPORT SYSTEMS AT HYDROTECHNICAL STRUCTURES
Get PDFNowadays, it is necessary to use monitoring and forecasting technologies for effective water resources management at water management facilities. The objective of this study is to develop and verify an integrated approach to water resources forecasting with the task of identifying features for forecasting, designing a data preprocessing submodule and a forecasting module. The workflow diagram of the water forecasting system includes sequential stages of data collection, preprocessing, filtering, feature extraction, and training. Sentinel-2 and MODIS satellite sources were used for data preprocessing. Predictors for the formation of time series by normalized difference water index (NDWI) and water surface temperature (LST) were selected in the feature engineering stage. The XGBoost Regressor algorithm was chosen due to its ability to model nonlinear relationships and feature interactions. Excluding winter months improved the model performance for all metrics, which demonstrates the importance of seasonal filtering when working with optical satellite data. The machine learning algorithm takes into account the analysis of satellite data (NDWI and LST indices) through the Google Earth Engine (GEE) platform. Both seasonal and long-term dynamics of water volumes in the Tasotkel reservoir are monitored for the period from 2020 to 2024. In practice, image initial filtering submodules were developed using linear regression and the XGBoost model. Model trained without winter data shows high performance using Metrics Mean Absolute Error (MAE) of 52.793, Root Mean Squared Error (RMSE) of 60.276, coefficient of determination (R2) of 0.673 and Mean Squared Error (MSE) of 3633.252 metrics. However, a decrease in clarity was observed due to snow and ice on reflective properties in winter. For the purpose of rational water resources management, the combination of satellite images and machine learning algorithms in this study shows the prospects for development
DETECTION OF HATE SPEECH ON SOCIAL MEDIA UTILIZING MACHINE LEARNING
Get PDFThis article investigates the identification of hate speech on social media using machine learning and deep learning techniques. The research uses metrics such as F-measure, AUC-ROC, precision, accuracy, and recall assessing the effectiveness of various tactics. The findings indicate that deep learning models, particularly the bidirectional long short-term memory (BiLSTM) architecture, consistently outperform other methods in categorization tasks. The research emphasizes the importance of sophisticated neural network designs in identifying the intricacies of hostile and offensive content online. The study offers insights for promoting early identification and prevention of cyberbullying, improving secure and inclusive online environments. Future research may explore real-time detection systems, hybrid approaches, or the integration of complementary components to enhance and improve innovative technology in tackling this significant social issue.
A sample tweet was annotated by specialists who categorize tweets as hate speech, offensive language, or neutral. The researchers applied shallow learning methodologies and integrated word embeddings like Word2Vec and GloVe to enhance the efficacy of deep learning models. The results indicate that BiLSTM surpasses shallow learning methods in detecting hate speech on Twitter, highlighting the efficacy of deep learning approaches in recognizing and tracking hate speech on social media platforms. When comparing different deep learning and machine learning models on different datasets, the results reveal that deep learning techniques are usually more effective. A reasonably high level of accuracy is achieved by KNN and SVM among classical algorithms, whereas Naïve Bayes performs the poorest. While deep learning approaches provide better results, tree-based models such as Random Forest and Decision Trees offer more consistent accuracy. Models based on neural networks, such as LSTM, CNN, and BI-LSTM, perform well, with LSTM-based methods excelling in particular. The most successful strategy for classification problems is the model presented, which obtains the greatest accuracy, precision, recall, F1-score of 95%. The research aids in the development of advanced tools and methodologies to mitigate hate speech on social media and foster positive online interactions. Future research may investigate alternative deep learning architectures, such as transformers, to enhance hate speech detection efficacy. The advancement of interpretable AI methodologies for identifying hate speech and delivering transparent forecasts might enhance user confidence and facilitate better content moderation decisions
A STUDY ON THE EFFECTIVENESS OF THE INSTAGRAM APP IN DEVELOPING VOCABULARY OF STUDENTS
Get PDFThis study examines the effectiveness of Instagram as a tool to improve vocabulary learning among students, focusing solely on quantitative analysis. As social media platforms become integral to students' daily routines, Instagram’s visual and interactive features may offer unique benefits for vocabulary acquisition. The aim of this research is to determine whether Instagram enhances vocabulary retention and engagement compared to traditional learning methods. A quantitative method was applied, involving 60 students divided into four groups of 15 each. These groups engaged with vocabulary lessons delivered through Instagram posts, stories, and interactive quizzes over a twelve-week period. Pre/posttests were used to measure vocabulary retention, providing a clear comparison of learning outcomes.The data analysis revealed a statistically significant improvement in vocabulary retention across all groups, indicating that Instagram can serve as an effective supplementary tool for vocabulary development. Additionally, students who interacted more frequently with Instagram content demonstrated better performance, suggesting that consistent social media engagement positively influences learning outcomes. The study highlights the importance of visually appealing and context-rich content in improving memorization and understanding of new vocabulary. Interactive elements, such as polls and quizzes, were especially effective in fostering active learning and sustaining student interest. The findings suggest that Instagram’s accessibility and familiarity can help bridge gaps in traditional teaching methods, and it makes learning more relatable and enjoyable for students. Teachers may find it valuable to explore similar platforms to create engaging digital learning environments. This research underscores the need to integrate modern technology into education to maximize student participation and outcomes
FUSION VIEW-NET: DUAL-VIEW DEEP LEARNING FOR ROBUST MAMMOGRAPHIC BREAST CANCER CLASSIFICATION
Get PDFBreast cancer is still one of the top causes of cancer-related death for women globally, and better patient outcomes depend on early identification. Although mammography is the main imaging modality used for screening, the delicate nature of early clinical symptoms and inter-reader variability sometimes compromise diagnostic accuracy. We examine the application of deep convolutional neural networks (CNNs) to automated classification of mammogram images in this work. FusionView-Net (FV-Net) is also presented, a novel dual-view integration framework that combines data from mediolateral oblique (MLO) and craniocaudal (CC) views to improve diagnostic precision. To produce a more comprehensive depiction of the breast tissue than conventional single-view methods, FV-Net combines contextual and spatial data from both standard perspectives. Two publicly available mammography datasets, which have been properly divided to allow for both seen-unseen data configurations and cross-dataset generalization testing, are used to assess the approach. A variety of CNN architectures are evaluated on separate and combined datasets, including ResNet18 and a specially created CNN. Findings indicate that FV-Net significantly increases model robustness and classification accuracy, as evidenced by consistently better F1 scores and ROC AUC values, especially when combined with ResNet18 and the custom CNN. The necessity for flexible models in actual clinical settings is shown by generalization studies, which further highlight the significance of dataset diversity by showing a noticeable drop in performance when domain shifts are present. Our results demonstrate how well multi-view fusion works for CNN-based mammography classification and provide useful guidance for choosing architectures and training methods. The development of trustworthy, broadly applicable AI technologies to assist radiologists in the early diagnosis of breast cancer is made possible by FV-Net
ALGORITHMS OF NP-COMPLETE PROBLEMS. PART II
No full textThis paper presents an analytical and algorithmic framework for solving NP complete problems, specifically focusing on the Subset Sum Problem (SSP). The study aims to develop polynomial time algorithms capable of efficiency identifying a k-element subset from an n-element set of positive integers, where the sum of the elements equals a predefined certificate. In an n-element set of positive integers without repetition, the goal is to find a k-element subset ( ), whose sum of elements is equal to the certificate . In this second part of the work, a sample of a subset with odd power is considered (in the first part - a sample of with even power which determines the complexity of the proposed algorithms for solving the subset sum problem. The obtained USPTO patents [20] present a computer system for ultra-fast processing of big data with a volume of finite and a processing speed proportional to the execution time T with the required memory for power k=3. The proposed approach is based on the mapping , the arguments of which are the certificate and the elements of the set and the union of the required subsets obtained from the two-dimensional array from the set taking into account the mapping and the given certificate Then the sampling time of the subset of odd cardinality with the given certificate and the required space satisfy the conditions T , which are obtained based on solving the problem of the sum of the required subset from the set of natural numbers . Overall, the findings establish a theoretical foundation for ultra-fast computing systems and data-intensive applications, aligning with modern computational complexity and big data paradigms.