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IJLAI Transactions on Science and Engineering 2024年第2期2卷 45-50页

作者： Meirambekov Adlan Hang Li Department of applied mathematics and informatics Karaganda Buketov University100000 KaragandaKazakhstan Software College Shenyang Normal University110034 ShenyangChina

In today’s educational environment,educators face challenges in manually crafting various documents,leading to inefficiencies and time *** address this,our study introduces a novel software solution designed to automate document creation and standardize processing in academic *** Python and LaTeX technologies,particularly the PyLaTeX library,the application streamlines document workflows by generating templated *** user input,the application automates document structure and formatting,reducing preparation time and *** by Python,LaTeX,and PyLaTeX,alongside ElectronJS,JavaScript,and Bootstrap for the frontend,our research encompasses a comprehensive methodology,including literature review,software design,experimentation,and efficiency *** analysis underscores the application’s effectiveness,demonstrating significant time savings and productivity *** improving document workflows,our research contributes to advancing educational practices and lays the groundwork for future investigations in this domain.

关键词： Document automation standardization educational institutions software development Python PyLaTeX efficiency productivity user interface workflow optimization

Geochemical prerequisites for the formation of oil and gas accumulation zones in the South Turgay basin,Kazakhstan

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Acta Geochimica 2024年第3期43卷 520-534页

作者： Rima Kopbosynkyzy Madisheva Vassiliy Sergeevich Portnov Gulmadina Bulatovna Amangeldiyeva Akmaral Bakhytbekovna Demeuova Yessimkhan Sherekhanovich Seitkhaziyev Dulat Kalimovich Azhgaliev Department Geology and Exploration of Mineral Deposits Abylkas Saginov Karaganda Technical University NPJSCKaragandaRepublic of Kazakhstan Laboratory of Geochemical Studies of Oil Water and RockAtyrauRepublic of Kazakhstan Geological and Mineralogical Sciences Atyrau University of Oil and Gas Named After SafiUtebayevAtyrauRepublic of Kazakhstan

This study predicts favorable oil and gas source-rock formation conditions in the Aryskum Depression of the South Turgay Basin,*** study assesses the thermal maturity and characteristics of organic matter by determining its environmental conditions using data from geochemical analysis of core(pyrolysis)and oil(biomarkers and carbon isotopic compositions)*** to the geochemical parameters obtained by pyrolysis,the oil generation potential of the original rocks of most studied samples varies from poor to *** facies–genetic organic matter is predominantly humic and less frequently humus–sapropel,indicating organic matter accumulation in the studied samples were under moderately reducing conditions(kerogenⅢand Ⅱ types)and coastal–marine environments(kerogen typeⅠ).The carbon isotopic compositions of oils derived from the Jurassic deposits of the Aryskum Depression also indicate the sapropelic and mixed humic–sapropelic type of organic matter(kerogenⅡandⅠ).Biomarker analysis of oils indicates original organic matter formation in an anoxic environment.

关键词： South Turgay Basin Oil and gas potential Source rock Organic matter Kerogen

Multimodal Deep Neural Networks for Digitized Document Classification

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Computer Systems Science & Engineering 2024年第3期48卷 793-811页

作者： Aigerim Baimakhanova Ainur Zhumadillayeva Bigul Mukhametzhanova Natalya Glazyrina Rozamgul Niyazova Nurseit Zhunissov Aizhan Sambetbayeva Department of Computer Engineering Khoja Akhmet Yassawi International Kazakh-Turkish UniversityTurkistanKazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstanaKazakhstan Department of Information and Computing Systems Non-Profit Joint Stock Company Abylkas Saginov Karaganda Technical UniversityKaragandaKazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan

As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially *** respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human *** intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years *** learning aids in comprehending input patterns so that object classes may be *** segmentation process divides the input image into separate segments for a more thorough image *** study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher *** further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student ***,a deep learning model based on Conv2D layers is proposed for the document classification *** the final part of this research,the proposed model is evaluated and compared with other machine-learning *** results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,*** achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.

关键词： Document categorization deep learning machine learning classification digitization

One Dimensional Conv-BiLSTM Network with Attention Mechanism for IoT Intrusion Detection

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Computers, Materials & Continua 2023年第12期77卷 3765-3781页

作者： Bauyrzhan Omarov Zhuldyz Sailaukyzy Alfiya Bigaliyeva Adilzhan Kereyev Lyazat Naizabayeva Aigul Dautbayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan Department of Information Technology and Security Abylkas Saginov Karaganda Technical UniversityKaragandaKazakhstan Department of Computer Science and Information Technology K.Zhubanov Aktobe Regional UniversityAktobeKazakhstan Department of Information Systems International Information Technology UniversityAlmatyKazakhstan Department of Computer Science Korkyt Ata Kyzylorda State UniversityKyzylordaKazakhstan

In the face of escalating intricacy and heterogeneity within Internet of Things(IoT)network landscapes,the imperative for adept intrusion detection techniques has never been more *** paper delineates a pioneering deep learning-based intrusion detection model:the One Dimensional Convolutional Neural Networks(1D-CNN)and Bidirectional Long Short-Term Memory(BiLSTM)Network(Conv-BiLSTM)augmented with an Attention *** primary objective of this research is to engineer a sophisticated model proficient in discerning the nuanced patterns and temporal dependencies quintessential to IoT network traffic data,thereby facilitating the precise categorization of a myriad of intrusion ***:The proposed model amal-gamates the potent attributes of 1D convolutional neural networks,bidirectional long short-term memory layers,and attention mechanisms to bolster the efficacy and resilience of IoT intrusion detection systems.A rigorous assessment was executed employing an expansive dataset that mirrors the convolutions and multifariousness characteristic of genuine IoT network settings,encompassing various network traffic paradigms and intrusion ***:The empirical evidence underscores the paramountcy of the One Dimensional Conv-BiLSTM Network with Attention Mechanism,which exhibits a marked superiority over conventional machine learning ***,the model registers an exemplary AUC-ROC metric of 0.995,underscoring its precision in typifying a spectrum of intrusions within IoT ***:The presented One Dimensional Conv-BiLSTM Network armed with an Attention Mechanism stands out as a robust and trustworthy vanguard against IoT network *** prowess in discerning intricate traffic patterns and inherent temporal dependencies transcends that of traditional machine learning *** commendable diagnostic accuracy manifested in this study advocates for its tangible *** investigation indubitably a

关键词： Intrusion detection attention deep learning IoT CNN BiLSTM

Influence of catalytic systems on process of model object hydrogenation

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International Journal of Coal Science & Technology 2014年第1期1卷 88-92页

作者： Murzabek Ispolovich Baikenov Gulzhan Gausilevna Baikenova Bolat Shaimenovich Sarsembayev Alma Baimagambetova Tateeva Almas Tusipkhan Aisha Zharasovna Matayeva karaganda State University Karaganda 100028Kazakhstan

On the basis of b-FeOOH,Fe(OA)_(3),Fe_(3)O_(4) iron and spherical catalysts NiO/SiO_(2),Fe_(2)O_(3)/SiO_(2) derived from slag waste coals of heating electrical stations,the hydrogenation of model polycyclic hydrocarbon at presence of nanodimensioned catalysts antracene was *** the example of conversion of anthracene,it was shown that upon release of hydrogenation of the product yield and degradation of polycyclic hydrocarbons in the hydrogenation,the mentioned catalyst systems appeared to be in the following order:nanoparticles b-FeOOH,Fe(OA)_(3) and Fe_(3)O_(4)[spherical catalysts NiO/SiO_(2),Fe_(2)O_(3)/SiO_(2)[commercial cobalt-molybdenum *** results showed that the catalysts studied are promising catalysts for the hydrogenation of polycyclic hydrocarbons and may be used for direct coal liquefaction.

关键词： Antracene Multi-ring hydrocarbons Nanocatalysts Spherical catalysts Hydrogenation

Integrating the geology, seismic attributes, and production of reservoirs to adjust interwell areas: A case from the Mangyshlak Basin of West Kazakhstan

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Applied Geophysics 2021年第3期18卷 420-430,434页

作者： Arslan Zhumabekov Zhen Liu Vasily Portnov Xiaodong Wei Xin Chen State Key Laboratory of Petroleum Resource and Prospecting in China University of Petroleum Beijing 102249China karaganda State Technical University Karaganda100027Kazakhstan BGP Inc. China National Petroleum Company CNPCZhuozhou 072751China

Dynamic models of the seismic,geological,and flow characteristics of a reservoir are the main tool used to evaluate the potential of drilling new infill *** geological models are mainly based on borehole data combined with dynamic analyses of production *** are used to determine the redevelopment of and adjustments to new drilling locations;however,such models rarely incorporate seismic ***,it is difficult to control the changes in geological models between wells,which results in the configuration of well positions and predicted results being less than *** improve the development of adjusted areas in terms of their remaining oil contents,we developed a new integrated analysis that combines static sediment modelling,including microfacies analysis(among other reservoir and seismic properties),with production ***,we illustrate this new process by(1)establishing favourable areas for static geological analysis;(2)studying well recompletion potential and the condition of non-producing wells;(3)conducting interwell analyses with seismic and sedimentary data;(4)identifying potential sites constrained by seismic and geological studies,as well as initial oilfield production;(5)providing suggestions in a new well development plan.

关键词： Reservoir geological model sedimentary facies seismic attributes well development remaining oil

Global Estimation of the Cauchy Problem Solutions’the Navier-Stokes Equation

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Journal of Applied Mathematics and Physics 2014年第4期2卷 17-25页

作者： A.A.Durmagambetov L.S.Fazilova Buketov karaganda State University KaragandaKazakhstan

The analytic properties of the scattering amplitude are discussed, and a representation of the potential is obtained using the scattering amplitude. A uniform time estimation of the Cauchy problem solution for the Nav... 详细信息

关键词： Schrodinger’s Equation Potential Scattering Amplitude Cauchy Problem Navier-Stokes Equations Fourier Transform