DSpace Repository :: Browsing 03. Bachelor's Thesis by Title

03. Bachelor's Thesis

Permanent URI for this collection

https://nur.nu.edu.kz/handle/123456789/8137

Browse

Now showing 1 - 20 of 150

Restricted
ACTION-DRIVEN TACTILE OBJECT EXPLORATION FOR SHAPE RECONSTRUCTION VIA OPTICAL TACTILE SENSORS
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Mussin, Tleukhan
We introduce an action-driven tactile exploration system using novel optical tactile sensors integrated into the gripper of a robot arm. These sensors consist of multiple silicone layers, with one layer featuring alternating yellow and red patterns. When this layer deforms — typically by stretching and reducing in thickness—the colored patterns shift. These changes are captured by an onboard camera and analyzed using a Convolutional Neural Network (CNN) algorithm. The gripper for the sensor was specifically designed and 3D printed to ensure the sensors operate correctly. The colored part of the sensor was isolated from the external light. We tested the sensor’s effectiveness in edge detection and localization using four different geometric objects. We evaluated our system using a diverse collection of objects in both medium and large sizes.
Restricted
ADAPTING TO LEARNER’S COGNITIVE DIFFERENCES USING REINFORCEMENT LEARNING
(Nazarbayev University School of Engineering and Digital Sciences, 2023) Nurgazy, Symbat; Issa, Ilyas; Kassymbekov, Saparkhan; Kuangaliyev, Zholaman
Restricted
ADVANCING BLOOD SAMPLE ANALYSIS: INCORPORATING EXPERT OPINIONS AND EXPLAINABLE AI IN MULTI-LABEL DISEASE PREDICTION
(Nazarbayev University School Engineering and Digital Sciences, 2024-04-19) Orynbay, Sultan; Akanova, Inabat; Turmakhan, Diana; Beken, Ulpan; Serikkazhy, Islam
Blood sample analysis plays a crucial role in modern medical practice, aiding in the detection of a wide array of diseases. Despite its significance, the potential of blood samples for predicting various diseases has remained largely unexplored. Our project aimed to dive into evaluate the efficacy of blood samples in predicting a broad spectrum of disease using large-scale MIMIC III medical dataset. Given the sparse nature of the data, we combine imputation with multi-task models for which we identify and utilize meaningful auxiliary tasks and are thus able to reach an average state-of-the-art ROC-AUC score of 81% across the 50 most prevalent diseases within the dataset. To further validate our findings, we sought the expertise of five medical doctors, who independently rated the predictability of these diseases from blood samples. Spearman’s rho analysis revealed a substantial agreement ( = 0.61) between the doctors’ ratings and the actual ROCAUC values of our machine learning models. In order to add transparency and reliability, we employed the Local Interpretable Modelagnostic Explanations (LIME) method to identify the most predictive blood sample features. These findings were rigorously cross-checked with medical experts, affirming the robustness and credibility of our predictive models. Our study represents a significant advancement in the field of medical diagnostics, showcasing the untapped potential of blood sample analysis in disease prediction. By integrating cuttingedge machine learning techniques with expert validation, we pave the way for enhanced patient care and improved healthcare outcomes.
Restricted
Aerodynamic analysis of wind farms
(Nazarbayev University School of Engineering and Digital Sciences, 2019) Duisenova, Alina; Badanova, Nazym
Wind energy is one of the most promising types of renewable energy and is successfully integrated in our lives. Wind turbines were used in the past centuries, but utilizing the wind energy in a large amount started with the installation of thousands of wind turbines in California in the late 1980s ("History of wind power", 2019). Although the wind energy became popular there are problems causing the wind energy usage to lag behind the wind range of traditional fossil fuel usage. That is, wind turbine operation is not continuous as required because of the failures that cause unscheduled downtime during their intended design lifetime. These failures mainly include the component failures, especially the rotor blades. Rotor blades are ones of the most critical components, what is being verified by the statistics showing 3800 incidents of blade failure each year out of an estimated 700 000 blades operating globally (Dvorak, 2019). The reasons and technical information about such structural failures of rotor blades are not discussed in the media and rarely reported in academic literature because of the unavailability of the technical data due to commercial confidentiality. Meanwhile, this problem became the topic of a great interest for many researchers all around the world.
Open Access
AI LIBRA VIRTUAL LIBRARY ASSISTANT: INTELLECTUALIZATION OF THE NU LIBRARY WEB-PORTAL USER INTERFACE ON THE BASIS OF AI APPLICATIONS
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Toktassyn, Altay; Unaspekov, Timur; Slamkhan, Sat; Gabdullin, Sirazh; Kenzhebayev, Alisher
This project is made to improve the existing library system of Nazarbayev University. Dr. Askar Boranbayev and Dr. Piotr Lapo offered us to take on this project. Our solution strives to make the NU library system convenient and efficient for students and faculty members. Our goal is to improve the user experience when working with the library system, make it more interactive, and also reduce the time it takes to find a book that matches the user’s interests. Former NU Library Director, Dr. Piotr Lapo, together with our professor Dr. Askar Boranbayev, advised us to use artificial intelligence based on their experience. Our team, together with our advisors, came to a unified implementation. We have introduced a virtual assistant. Firstly, it performs the function of voice announcements on the NU library website, using the Google Cloud Platform. Secondly, our assistant answers arbitrary questions from users regarding the library, based on the "spaCy" model. We also developed our own model to improve the skill of working with Natural Language Processing tasks. But in the end, we came to the conclusion that spaCy works better. We used a database of answers to frequently asked questions from the NU library website to train our model. Thirdly, we implemented a smart book recommendation system so that the user can get information about the book based on his interests and other users' reviews. Our solution works in the format of a Backend web application, which in the future will be integrated with the NU library system. But while we have not integrated with the NU library system, we have written our own Frontend application for simulation in order to fully test it and demonstrate the results. Frontend is written in React. The backend is written using Fast API. PostgreSQL was used to manage the database. It should be noted that our team led by Dr. Askar Boranbayev presented this project at the International Scientific and Practical Conference "Industrial Development: Technologies for People and Services in the Era of Innovation".
Embargo
ANONYMOUS PUBLICATION SUBMISSION AND REVIEW PLATFORM USING ML
(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-25) Aitymbetov, Nurmukhammed; Satkan, Shyngys; Bekmukhanbetov, Dastan; Suiirkhanov, Meiirlan; Zormpas, Dimitrios
We created an anonymous publication submission and review platform that uses an effective machine learning model to protect the authors' privacy and the integrity of the review process. The platform is designed with Django and the Django REST Framework (DRF) for backend operations, React for the frontend interface, and PyTorch for training machine learning model. Our technology automates the process of connecting papers with the most appropriate reviewers based on their expertise, reducing human interference and any bias. Furthermore, it makes it easier to provide feedback to assigned publications using an intuitive interface. We believe that our website would significantly contribute to the academic publication review process.
Embargo
APPLICATION OF DEEP NEURAL NETWORKS AND COMPUTER VISION IN REHABILITATION ROBOTS
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Gimalay, Ibragim
The objective of this research is to develop an automated system for detecting gait-related health issues using Deep Neural Networks (DNNs). The system processes video footage of patients to estimate their 3D body posture through a DNN-based method, then this 3D body posture gets classified using another DNN-based method. The analyzed 3D body pose data is classified into 3 categories: Healthy, Parkinson’s disease and Post Stroke. This technology eliminates the need for bulky, complex equipment and extensive lab space, making it practical for use at home. It also doesn't require specialized knowledge for feature engineering, as it automatically extracts meaningful, high-level features from the data. The test results show classification accuracies ranging from 56% to 96% across different groups. The conclusion of this study indicates that this system is a promising tool for automatically classifying gait disorders and could be a foundational technology for future deep learning applications in clinical gait analysis. The significance of this system is underscored by its use of digital cameras as the sole required equipment, facilitating its use in patient homes and among the elderly for regular monitoring and early detection of gait changes.
Restricted
“AUTISMSPEAKS: CREATION OF A DIGITAL PECS APP”
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Kamaliden, Akmaral; Mussekenova, Assem; Ainukatov, Yernar; Sandygulova, Anara; Isteleyev, Marat
People diagnosed with Autism spectrum disorder or ASD face issues with social interaction due to their limited abilities to communicate. This is a problem because ASD patients may be developed according to their age, but have poor social and emotional capabilities that restrict their opportunity to interact with society. Therefore, various communication strategies are used. For example, Picture Exchange Communication System (PECS) is a methodology created in order to promote communication of ASD patients, develop speech and their ability to share feelings and needs. It is based on the usage of physical cards with images to construct sentences. However, there are several limitations. Physical cards have only a text name of image and a lack of spoken words or audible response hinders the learning of language and speech. Furthermore, traditional PECS has physical limitations in terms of the amount of cards that can be carried by patients. Such issues can be solved by the creation of digital PECS in a form of mobile application with a broad image library and Text-to-Speech (TTS) system. PECS and related applications have already been created. However, they do not support Kazakh language and can not be accessed through various types of devices, and this causes inconvenience for users. In addition, the existing applications use illustrations for cards, while using realistic images seems to be more efficient in learning. Therefore, our aim is to consider the issues mentioned above and to develop a multi-user PECS application with a library of actual images in three languages.
Restricted
BABY CRY CLASSIFICATION AND ANALYSIS
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Yergesh, Beibit; Zharas, Galymzhan; Uisengali, Dariya; Nurmukhanov, Alisher; Sagatbekov, Dinmukhammed
we have developed a mobile application that is dedicated to parents and baby carers of newborn babies. The main feature of the app is that it can classify the baby cry sound through use of mobile phones and tell the reason for the baby cry. Moreover, the system includes many good helping features for parents and baby carers such as sleep sounds page, blog page dedicated for care of newborn babies and it maintains the history of cry analysis for every user. Moreover, the system contains a very user friendly design to make it easier to navigate for different group of users inside our mobile application.
Open Access
BAMBOOK: PERSONALIZED BOOK RECOMMENDATION AND ENGAGEMENT PLATFORM
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Nurtayev, Nurzhan; Saiynov, Dias; Shayakhmet, Yeldar; Amanbek, Dauren; Shayakhmet, Shakhnazar
In this report we are going to present a developed book recommendation mobile application system “BamBook”. Purpose of BamBook is to address the gap in digital platforms offering comprehensive book recommendations. Our mobile application is powered by Go, Python and Swift programming languages, while utilising Google’s YouTube Retrieval model for recommendation feature, trained on GoodReads dataset. In the process of development, we encountered challenges mostly in choosing, integrating the model, handling extensive datasets and optimising for the iOS platform. Our system architecture was improved and an iterative method was used to overcome these problems. Although our product still needs more improvements in both back and front parts, evaluation from user’s shows that BamBook meets our main objective which is to provide for users an application with easily understandable interface and relevant recommendation.
Restricted
“BILIM SOURCE": WEB-BASED K-12 DIGITAL RESOURCE LIBRARY
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Alikhanov, Nurali; Khajimetova, Shakhruza; Naumagambetova, Madina; Nurzhakyp, Darkhan; Shamshiden, Alua
Kazakhstan has a substantial portion of its population composed of children, with approximately 31% of the populace under the age of 17 (Eurasia Expert, 2023). There is a consistent annual rise in its birth rate, which emphasizes the need to promote the focus on the education system as a crucial factor in the country's development. Currently, the educational sector faces challenges, and one of these issues is the quality of teacher training (Abishev, 2020). To address this concern, several governmental programs have been initiated. Notably, the Nazarbayev Intellectual Schools educational project was established, introducing a new teaching format. Teachers selected to work in these schools enhance their knowledge through continuous professional development such as training sessions and workshops. As a result, 90% of NIS students receive scholarships at further educational institutions. Many of them participate in international competitions and apply to foreign universities (Eldesov, 2022). However, the NIS program does not fully resolve the issue of quality education for Kazakhstani children, as only 2% of children in Kazakhstan attend NIS schools. These schools are not available in rural areas, with only 21 schools across the country, leading to limited enrollment (Wikipedia contributors, 2023). Additionally, the budget allocated to them faces criticism, as there is a considerable difference compared to the average school budget - in 2020-2021, the NIS school got 23 times more financial budget than the average school (Wikipedia contributors, 2023). Digitalization of resources, including platforms like BILIM Land, Online Mektep, and others, helps address limited access to educational materials. However, despite numerous online resources for children, there is a lack of services designed for teachers. As part of the government program "Digital Kazakhstan," digitalization is expected to continue assisting educators in their jobs. To bridge this gap, we propose the "Bilim Source" project, an online portal facilitating collaboration and sharing of high-quality educational materials among teachers. This platform aims to connect educators with various teaching methodologies gained through special training, workshops, or experience, enabling them to share their materials with others.
Embargo
BLIND SOURCE SEPARATION FOR AUTOMATIC MUSIC TRANSCRIPTION
(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-01) Kurmangaliyev, Bauyrzhan
The primary objective of this project is to develop methods aimed to the conduct the blind signal separation of musical notes with Nonnegative Matrix Factorization (NMF). This is motivated by the fact that music signals are often recorded with a single microphone, hence, there is a need to develop the Automatic Music Transcription (AMT) methods that could mitigate this assumption and produce the desirable separation result. Therefore, this project report presents the rank estimation method for determination of number of musical notes in the recording. It is motivated by the fact that most of the research works on NMF assume \emph{a priori} knowledge regarding the rank of factorization which may not be available in most of the real world scenarios. As a result, the Weighted Singular Value Thresholding based on Stein's Unbiased Risk Estimate (WSVT-SURE) in which rank estimation is performed by non-uniform shrinkage of singular values via weight vector is presented. We also introduce gradient optimization of a smooth approximation of WSVT-SURE (GWSVT-SURE) to estimate the optimal threshold parameter. In the context of AMT, the proposed algorithms allow one to estimate the number of musical note components in the recordings. The proposed algorithms have been evaluated with the polyphonic piano music excerpts. It is observed that the proposed WSVT-SURE algorithm reaches significant improvement in the estimation performance, while GWSVT-SURE shows substantial savings in the computational cost.
Embargo
BUTLER MATRIX MINIATURIZATION FOR 5G COMMUNICATIONS AND BEYOND
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Amangeldi, Yerassyl
This capstone project presents a miniaturized implementation of Butler’s beamforming array on microstrip technology. Firstly, the basis of size reduction is obtained by developing a structure that behaves exactly as the quarter-wave transmission line segment at the operation frequency. Secondly, this structure is applied to reduce the area of higher-order components such as 3dB hybrid coupler and crossover. Then, the components are assembled into the layout of Butler Matrix where several components are reduced in size further. The obtained final structure takes up approximately two times less area at the cost of 20% theoretical bandwidth reduction. A 4-by-4 experimental setup is developed for validation at 2.5 GHz which corresponds to the lower 5G frequency band.
Open Access
COMPUTATIONAL ANALYSIS OF FLUID STRUCTURE INTERACTION (FSI) IN HORIZONTAL AXIS WIND TURBINES (HAWTS)
(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-02) Makshatov, Madi; Nurzhanova, Diana; Sataibekova, Aruzhan; Kussinov, Zhanibek
Wind power plays a crucial role in the worldwide shift towards sustainable and renewable sources of energy. Wind turbine power generation performances made them a widely adopted method for electricity production, playing a crucial role in the world’s energy resources. Accordingly, optimizing the wind turbine blade’s design is essential for increasing wind turbine performance and reducing expenses. The main aim of this capstone project is to analyze the Fluid Structure Interaction of the HAWTs and to achieve the most effective design of the turbine, in terms of power generation performance and resource requirement by optimizing the blades using low fidelity methods. In engineering and scientific studies, low-fidelity and high-fidelity simulation and optimization have become common concepts, especially in the field of wind turbine design and analysis. These concepts are essential in order to study computational structure and controlling the resource demand, as well as improving the operation of the turbines. This paper focuses on applying low-fidelity optimization techniques with QBlade, which is a commonly used open-source software for creating aerodynamic simulations of horizontal axis wind turbines. A low-fidelity simulation can involve simplified fluid dynamics calculations and simplified structural models, in the context of wind turbine design, in order to forecast the turbine’s effectiveness. Compared to the high-fidelity simulations, low fidelity simulations are economically and computationally reasonable. It means that, in the process of optimization of design parameters of the wind turbine more design alternatives are available in order to reach the most effective parameters. Computational Analysis of Fluid-Structure Interaction (FSI) within Horizontal Axis Wind Turbines (HAWTs) will be studied on the NREL 5MW and NREL Phase VI turbine. In order to get the optimization results of these wind turbine blades, low fidelity optimization methods, such as Betz and Schmitz theories will be used.
Restricted
COMPUTATIONAL CHEMISTRY FOR IMPROVED NATURAL COMPOUNDS-TARGET AFFINITY PREDICTIONS
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Sabyrbek, Aruzhan; Gole, Daria; Bolatov, Arman; Nurbayev, Zhanbolat
The rapid evolution of pathogens underscores an urgent need for accelerated therapeutic development strategies. With an emphasis on natural compounds, this work expands the field of drug repositioning by employing machine learning(ML) techniques to forecast compound-protein interactions that may have therapeutic consequences. Our method makes use of several pre-trained Drug-Target Affinity (DTA) models, such as GraphDTA, MLT-LE, and DeepDTA, to predict binding affinities between protein targets listed in BindingDB and natural products sourced from the COCONUT database. This integration aims to create a robust database facilitating the repurposing of naturally occurring compounds, which are often overlooked in traditional synthetic drug pipelines.
Open Access
Construction of steel plate I-girder bridge in Jeonju, South Korea
(Nazarbayev University School of Engineering and Digital Sciences, 2018-04-16) Karymsakov, Nariman; Yesbergen, Baizhan; Amangeldy, Kanat; Sadyrbayeva, Akbota; Kenzheshova, Akgul
Designing a bridge is one of the complex engineering problems. In order to design a bridge, an engineer firstly needs to choose the type of bridge he is going to construct, and in the process of a design determine the most significant factors in the analysis and selection, and to develop a comprehensive understanding in designing a bridge. In this paper, our group has mainly focused on the structural part of the bridge such as analysis of the loads, and behavior of the bridge under stresses. It is also essential to understand material characteristics and behavior under the loads in order to maintain stability and duration. The specifications of the design criteria are met according to the AASHTO code.
Open Access
CREATION OF A MOBILE APP: KAZAKH NATIONAL GAME - TOGYZ QUMALAQ
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Saduakhas, Sanzhar; Tarikh, Damir; Taniyev, Kenzhebek; Saiyngali, Myrza; Tangatarov, Nurmukhammed
Our project focuses on preserving and improving the appreciation of Kazakh culture, particularly through the national game ”Togyz Qumalaq.” We are addressing the challenge of cultural practices being overshadowed by modern advancements by creating an interactive mobile application. This initiative is crucial for keeping ”Togyz Qumalaq” noted and accessible in the future. The mobile application features include: • A modernized digital version of the traditional ”Togyz Qumalaq” game board. • An AI offering three levels of difficulty to suit all players. • Simple, intuitive gameplay designed to be accessible to newcomers. • An online multiplayer function to promote global interaction and cultural sharing. By transforming ”Togyz Qumalaq” into a digital format, our project not only preserves this important aspect of Kazakh heritage but also makes it engaging and educational for users worldwide.
Restricted
CREATION OF MOBILE APP: GAMIFICATION OF NU COURSES
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Suleimen, Arna; Karabay, Saltanat; Amantayeva, Gulden; Sumbekova, Assylzhan; Karabay, Saltanat
The "Creation of Mobile App: Gamification of NU Courses" project offers a proactive approach to addressing the widespread problem of student disengagement in traditional learning settings. Designed specifically for NU students and instructors, this mobile application introduces gamification elements to elevate the learning experience. Through a combination of mini-games like trivia, matching definitions and hangman, leaderboards, and progress tracking, the app aims to motivate students and reinforce their grasp of course material. Instructors benefit from features allowing them to easily manage courses, upload resources, and utilise AI-driven chatbots for enhanced student support. Developed using Flutter, a versatile cross-platform framework, the app emphasises accessibility and user-friendly design. The forthcoming evaluation phase will involve comprehensive user testing with NU students and instructors, with a focus on assessing engagement levels, usability, and the impact of gamification in improving learning outcomes.
Open Access
Deep Learning-Based Wind Speed Prediction for Optimized Wind Turbine Operation
(Nazarbayev University School of Engineering and Digital Sciences, 2024-05-03) Alimukhambetova, Sofiya; Kochkarova, Ayana
Wind energy has been a promising source of clean energy that does not negatively affect our environment. Because of the fluctuations in wind speed, it is crucial to predict its values for wind turbines to have the maximum effective power output. This project aims to develop a way for short-term wind speed prediction based on deep learning technologies, such as CNN, LSTM, RNN, and GRU models, alone and in combination. Through iterative experimentation and evaluation, we develop ten final models and assess their performance based on Mean Squared Error (MSE), score, and computational efficiency. Our findings reveal that the GRU model achieves the highest performance with a MSE of 0.00238 m/s and R2 score of 0.8796. Additionaly, the similarly structured LSTM model demonstrates superior computational efficiency along with high R2 value, outperforming GRU model. By examining the performance of multiple deep learning architectures, the project seeks to identify the most suitable approach for wind speed prediction, thereby facilitating more efficient and sustainable utilization of wind resources for power generation.
Open Access
DESIGN A MULTI-STORY OFFICE BUILDING IN SAN FRANCISCO, CALIFORNIA USA
(Nazarbayev University School of Engineering and Digital Sciences, 2017) Abildinov, Tanat; Bekmurat, Altynay; Irsainova, Alina; Izimova, Altynay; Kulchmanov, Alen
San Francisco is rapidly growing financial and economic center of the region with increasing demand of the office area. This document contains the design of reinforced concrete moment frame office building in a high seismic hazardous region of San Francisco, California, USA. The project is named Sky City and performed by Trust Construction Company. Sky City is a 12 story high rise building with one underground parking floor, with the internal area of 15649 m2, located on the land area of 10,000 m2 in the Financial District of San Francisco. This document contains a detailed information of the global architectural design of the building based on the IBC with attached technical drawings of the first floor, typical floor, underground parking, site layout plans with indicated traffic flow and conceptual views, developed in accordance with CAD standards.