Research hour is focused on discussion of distributed systems used for Big Data Analytics. Where in the basis lays open-source platform Apache Hadoop and all the surrounding ecosystem which includes Apache Pig, Apache Hive, Apache Flume, Apache Spark, and some other Apache software. The aim is to understand and learn how to use all available tools for Big Data storage and analytics, compare them and find the best for various problems.
Attendees of the research hour are PhD, Master and Bachelor students whose research is focused on this area. So each week they share their progress and some insights to the application of distributed systems in different problems. Some results of this event are couple of publications in international conferences and local journals. By popularization of this topic we aim to prepare our students and colleagues for the Big Data Era and have ready tools and technologies to conduct some data science projects. The research hour is conducted during 15:00-16:00 period each Friday.
The IoT research group is currently a group of 4 people (1 Master and 2 PhD students and an istructor) whose research interests are in the field of Internet of Things (IoT). During the research hour each person speaks about the things he/she read, learned, discovered or made in this area within about 10-15 minutes and then 30 minutes discussion session is conducted. During the discussion session each person comments on what have been spoken before and may give some advice. Discussion also provides means for analysis of the current state of persons research work and to make some decisions on further work. After discussion a group work session is conducted for about 1 hour. During this time each person reads scientific paper about his topic or makes work related with his/her research work.
Main researches in this area are devoted to the consideration of different applied and research problems by using the main statistical learning techniques including linear and logistic regression, K-Means, Clustering and Nearest Neighbors, classification methods as well as non-linear methods such as Generalized Additive Models, Decision Trees, Boosting, Bagging and Support Vector Machines.
The development of this area relates to the increasing of the computing power over the last 20 years, so that highly computational methods of Statistical Learning have got a new life.
In particular the last decade has seen a significant expansion of the number of possible approaches. Since these methods are so new, the business and research community is generally unaware of their huge potential. The research topic may cover different applied areas such as Marketing, Statistics and other important business decisions. With the explosion of “Big Data” problems we with the group of PhD and Master Students consider various problems for education, security, medicine and some other domain.
The group started as a result of the research hours organized by Dr. Cemil Turan during the Fall-2018 semester. Several faculty staff, including Askhat Aitimov, Diana Burissova, Konstantin Latuta, Rashid Baimukashev, Ruslan Jantayev, and Zhasdauren Duisebekov, joined the group to study the general concepts within the Image Processing field. Afterwards, focus groups were formed consisting of 2-3 members, each team focusing on a specific area, specifically related to Face Recognition algorithms. The outcome of this intensive research were two publications in the IEEE international conference (ICECCO-2018). The participants had a great experience in group work, paper publishing, and conference participation. The regular weekly meetings included topic discussions in the field. Because of the common interest, it was decided that the research group focuses on the Computer Vision area, and the plan for the Spring-2019 semester was to come up with a program curriculum (syllabus) for the Computer Vision class in the future.
The long-term goal of the research is to develop algorithms and systems that will vastly improve a user's ability to find, absorb, and extract information from Natural Language (Kazakh Language). The group's research generally proceeds to develop techniques to address underlying theoretical problems in the syntactic, semantic and pragmatic analysis of natural language. As has become more or less standard in the field, we rely on pure NLP algorithms as our primary modeling tool, and combine them with statistical machine learning techniques.
Time and location: Every Wednesday, 11:00 at Meeting Room of Engineering Faculty.
Research group is currently a group of 4 people (1 Dr, 1 PhD student and 2 MSc students) whose research interests are in the field of ICT in Education, how to apply ICT in Education using Data Science. The group started as a result of the research hours organized by Dr. Meirambek Zhaparov during Fall 2018 semester.
The research group is focused on discussion of personality classification, data analysis and other topics in education by using ML algorithms.
The research hours are conducted during 14:00-16:00 period each Friday.
The research hours are devoted to construction algorithms for analytical solving systems of equations and finding analytical solution of Eigen values - Eigen vectors problems (аналитического решения проблемы собственных векторов- собственных значений). These algorithms are used for solving technical and economical problems (for analysis of models of processes in these spheres) and abilities of efficient control of these processes. The idea of constructing such algorithms is based on Frobenius matrices properties (на особенностях матриц Фробениуса).