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8 results on '"Assel Mukasheva"'

1. Analysis and Synthesis of Single-Bit Adders for Multi-Bit Adders with Sequential Transfers

Author

Sakhybay Tynymbayev, Assel Mukasheva, Kuanyshbek Ibragimov, Adil Mukhamedgali, Gani Sergazin, and Teodor Iliev

Subjects
single-bit adder, multi-bit adder with consecutive transfers, conditional sum adder, multiplexer, Engineering machinery, tools, and implements, TA213-215
Abstract

This paper provides an analysis of existing single-digit binary adders from the point of view of their implementation on fans built based on metal-oxide-semiconductor field-effect transistor—MOSFETs. The synthesis of a single-digit adder with a conditional sum is carried out. The considered adders are compared in terms of speed and hardware complexity (by the number of MOSFETs). Adders perform arithmetic operations on numbers. In combination with other logical operations, adders are the core of the circuits of arithmetic logic devices that implement several different operations; they are an integral part of different processors. The most important parameters of adders are their hardware complexity and performance; therefore, many options for single-bit and multi-bit connectors with serial, parallel and combined transmissions have been developed. In the final part, a scheme of a multi-bit adder with consecutive transfers on adders with a conditional sum is given. An example of performing addition operations is given.

Published
2024
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2. Modification of U-Net with Pre-Trained ResNet-50 and Atrous Block for Polyp Segmentation: Model TASPP-UNet

Author

Assel Mukasheva, Dina Koishiyeva, Gani Sergazin, Madina Sydybayeva, Dinargul Mukhammejanova, and Syrym Seidazimov

Subjects
deep learning, polyp segmentation, U-Net, atrous block, Engineering machinery, tools, and implements, TA213-215
Abstract

Colorectal cancer is the third most prevalent type of cancer globally, and it typically progresses unnoticed, making early detection via effective screening methods crucial. This study presents the TASPP-UNet, an advanced deep learning model that integrates Atrous Spatial Pyramid Pooling (ASPP) blocks and a ResNet-50 encoder to enhance polyp boundary delineation accuracy in colonoscopy images. We utilized augmented datasets from Kvasir-SEG and CVC Clinic-DB, which included up to 2000 images, to enrich the training examples’ variability. The TASPP-UNet achieved a superior IOU of 0.9276, compared to 0.9128 by the ResNet50-UNet and 0.8607 by the standard U-Net, demonstrating its efficacy in precise segmentation tasks. Notably, this model exhibited impressive computational efficiency with a processing speed of 151.1 frames per second (FPS), underscoring its potential for real-time clinical applications aimed at early and accurate colorectal cancer detection. This performance highlights the model’s capability not only to improve diagnostic accuracy but also to enhance clinical workflows, potentially leading to better patient outcomes.

Published
2024
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3. Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis

Author

Marlen Kossakov, Assel Mukasheva, Gani Balbayev, Syrym Seidazimov, Dinargul Mukammejanova, and Madina Sydybayeva

Subjects
machine learning, clustering, tuberculosis, data analysis, Engineering machinery, tools, and implements, TA213-215
Abstract

In many fields, data-driven decision making has become essential due to machine learning (ML), which provides insights that improve productivity and quality of life. A basic machine learning approach called clustering helps find comparable data points. Clustering plays a critical role in the identification of patient subgroups and the customisation of treatment in the context of tuberculosis (TB) research. While prior studies have recognized its utility, a comprehensive comparative analysis of multiple clustering methods applied to TB data is lacking. Using TB data, this study thoroughly assesses and contrasts four well-known machine learning clustering algorithms: spectral clustering, DBSCAN, hierarchical clustering, and k-means. To evaluate the quality of a cluster, quantitative measures such as the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index are utilised. The results provide quantitative insights that enhance comprehension of clustering and guide future research.

Published
2024
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4. Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System

Author

Karina Niyazova, Assel Mukasheva, Gani Balbayev, Teodor Iliev, Nazym Mirambayeva, and Mukhamedali Uzakbayev

Subjects
AML, swarm intelligence, ant colony optimization, Engineering machinery, tools, and implements, TA213-215
Abstract

The fight against financial crimes has become increasingly challenging, and the need for sophisticated systems that can accurately identify suspicious transactions has become more pressing. The goal of the study is to develop a new feature selection method based on swarm intelligence algorithms to improve the quality of data classification. This article is about the development of an information system for the classification of transactions into legal and suspicious in an anti-money laundering sphere. The system utilizes a swarm-algorithm-based feature selection approach, specifically the ant colony optimization algorithm, which was both used and adapted for this purpose The article also presents the system’s functional–structural diagram and feature selection algorithm flowchart. The proposed feature selection method can be used to classify data from various subject areas.

Published
2024
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5. The Development of a Secure Internet Protocol (IP) Network Based on Asterisk Private Branch Exchange (PBX)

Author

Mubarak Yakubova, Olga Manankova, Assel Mukasheva, Alimzhan Baikenov, and Tansaule Serikov

Subjects
Asterisk, Private Branch Exchange based on Internet Protocol (IP PBX), IP telephony, security, Session Initiation Protocol (SIP), Transport Layer Security (TLS), Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern is that with the increasing adoption of IP telephony worldwide, the network might be vulnerable to hacking and unauthorized access, posing a threat to the privacy and security of the transmitted information. This article proposes a solution to address the security concerns of the IP network. After conducting an experiment and establishing a connection between the two servers using the WireShark sniffer, a dump of real traffic between the servers was obtained. Upon analysis, a vulnerability in the network was identified, which could potentially be exploited by malicious actors. To enhance the security of the network, this article suggests the implementation of the Transport Layer Security (TLS) protocol. TLS is a cryptographic protocol that provides secure communication over a computer network, ensuring data confidentiality and integrity during transmission. Integrating TLS into the network infrastructure, will protect the telephone traffic and prevent unauthorized access and eavesdropping.

Published
2023
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6. Розробка системи діагностики цукрового діабету з використанням bigdata

Author

Assel Mukasheva, Sabina Rakhmetulayeva, Gulnar Astaubayeva, and Sergiy Gnatyuk

Subjects
Big Data, інформаційна система, treatment, Applied Mathematics, Mechanical Engineering, Energy Engineering and Power Technology, Industrial and Manufacturing Engineering, Computer Science Applications, цукровий діабет, information system, Diabetes mellitus, platform, MongoDB, Control and Systems Engineering, Hadoop, Management of Technology and Innovation, лікування, платформа, Environmental Chemistry, patient, Electrical and Electronic Engineering, пацієнт, database, Food Science, Python
Abstract

Diabetes is among the socially significant diseases, which leads to high costs for the diagnosis and treatment of diabetes. Diagnosis and treatment of diabetes is currently one of the important tasks in medicine at the present stage of development of medical services. An important direction in the development of medical services for the population is the development and implementation of various problem-oriented information systems. Similar systems developed earlier did not cover the entire amount of heterogeneous information that is collected when diagnosing and prescribing the course of diabetes treatment, nor did they use technologies and cloud services as tools for Big Data. In this article, let’s make use of the predictive analytic to forecast and categorize the type of diabetes which offers an effective method for treating and curing patients at a reduced cost, with improved results such as affordability and availability. An information system platform has been developed and configured to manage the Hadoop cluster, as well as a non-relational database that uses and processes unstructured data in various formats. All experimental research, development of methods and algorithms, as well as solving computational problems were implemented using software languages for application development. The novelty lies in the research of distributed computing models that provide efficient execution of developed algorithms using the conceptual model of the processes of search, extraction and analysis of unstructured data in large data sets. The practical implementation of algorithms was carried out on the basis of methods of object-oriented programming and object-oriented databases, Цукровий діабет належить до соціально значущих захворювань, що призводить до високих витрат на діагностику та лікування цукрового діабету. Діагностика та лікування цукрового діабету в даний час є одним із важливих завдань медицини на сучасному етапі розвитку медичної служби. Важливим напрямом розвитку медичного обслуговування населення є розробка та впровадження різноманітних проблемно-орієнтованих інформаційних систем. Подібні системи, розроблені раніше, не охоплювали весь обсяг різнорідної інформації, яка збирається при діагностиці та призначенні курсу лікування цукрового діабету, а також не використовували технології та хмарні сервіси як інструменти для роботи з великими даними. У цій статті ми використовуємо прогностичну аналітику для прогнозування та класифікації типу діабету, який пропонує ефективний метод лікування та лікування пацієнтів за зниженою ціною з покращеними результатами, такими як доступність та доступність. Розроблено та налаштовано платформу інформаційної системи для управління кластером Hadoop, а також нереляційною базою даних, яка використовує та обробляє неструктуровані дані у різних форматах. Всі експериментальні дослідження, розробка методів та алгоритмів, а також вирішення обчислювальних завдань було реалізовано з використанням мов програмування для розробки додатків. Новизна полягає у дослідженні моделей розподілених обчислень, що забезпечують ефективне виконання розроблених алгоритмів з використанням концептуальної моделі процесів пошуку, вилучення та аналізу неструктурованих даних у великих наборах даних. Практична реалізація алгоритмів здійснювалася на основі методів об'єктно-орієнтованого програмування та об'єктно-орієнтованих баз даних

Published
2022

7. A dynamic analysis of six-bar mechanical press

Author

Gulnara Abdraimova, Assel Mukasheva, Saduakhas Japayev, Batyrkhan Kyrykbaev, Algazy Zhauyt, Kuatbek Kozhamberdiyev, and Bagdat Uskembayeva

Subjects
Conservation of energy, Lever, business.product_category, Computer science, Materials Science (miscellaneous), Problem statement, Newton's laws of motion, Linkage (mechanical), Industrial engineering, Industrial and Manufacturing Engineering, law.invention, Moment (mathematics), law, Business and International Management, Deep drawing, business, Six-bar linkage
Abstract

This paper analyzes the dynamical behavior of a six-bar linkage used in mechanical presses for metal forming such as deep drawing. Raising the technical level of machines requires an expansion of technological capabilities of the equipment and devices of the existing structures, as well as their equipping with fundamentally new mechanisms. In this regard, the task of developing methods for the dynamic of complex flat lever mechanisms with the desired laws of motion of the input and output elements allowing automatizing the implementation of all design phases with the use of computer is quite relevant. The simulation results obtained with a Maple program are validated by comparing the theoretical values of the input moment with the ones obtained from the conservation of energy law. Content of the article is distinguished by serious problem statement, interesting theoretical study and its practical test. This article provides new scientific results that have theoretical and applied significance.

Published
2017
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8. Forecasting the Prevalence of Diabetes Mellitus Using Econometric Models

Author

Nurbek Saparkhojayev, Sanjay Kalra, Amy Apon, Zhanay Akanov, and Assel Mukasheva

Subjects
Endocrinology, Diabetes and Metabolism, Data analysis, 030209 endocrinology & metabolism, Disease, 030204 cardiovascular system & hematology, Gross domestic product, 03 medical and health sciences, 0302 clinical medicine, Diabetes mellitus, Scikit-learn, Environmental health, Internal Medicine, Gross Regional Product, medicine, Population growth, Activity-based costing, Original Research, business.industry, Statistics, Regression analysis, medicine.disease, Econometric model, business, Forecasting, Python
Abstract

Introduction The prevalence of diabetes in Kazakhstan has reached epidemic proportions, and this disease is becoming a major financial burden. In this research, regression analysis methods were employed to build models for predicting the number of diabetic patients in Kazakhstan in 2019, as this should aid the costing and policy-making performed by medical institutions and governmental offices regarding diabetes prevention and treatment strategies. Methods A brief review of mathematical models that are potentially useful for the task of interest was performed, and the most suitable methods for building predictive models were selected. The chosen models were applied to explore the correlation between population growth and the number of patients with diabetes as well as the correlation between the increase in gross regional product and the growth in the number of patients with diabetes. Moreover, the relationship of population growth and gross domestic product with the growth in the number of patients with diabetes in Kazakhstan was determined. Our research made use of the scikit-learn library for the Python programming language and functions for regression analysis built into the Microsoft Excel software. Results The predictive models indicated that the prevalence of diabetes in Kazakhstan will increase in 2019. Conclusion Mathematical models were used to find patterns in a comprehensive statistical dataset on registered diabetes patients in Kazakhstan over the last 15 years, and these patterns were then used to build models that can accurately predict the prevalence of diabetes in Kazakhstan.

Published
2019

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