Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: The Significance of Using Gamification Elements in the Educational Process
Abstract: Gamification elements are used in various fields. Game technologies can also be used in the educational process. How to make lessons interesting with the help of gaming technologies? The article provides an answer to this question. A geometry game was developed and the game mechanics were analyzed. Several solutions have been proposed to combat the destructive strategy. The main constructs of gamification are shown, such as intrinsic and extrinsic motivation, attitude, self-efficacy. On the basis of the developed game, experiments were carried out with students. On the basis of the experiment, the effectiveness of using gamification for the formation of sustainable internal motivation of students to systematically solve problems is shown. Examples are given that illustrate the content aspect of the presented model - fragments of author's developments. The application of the developed game as a material for repeating the topic for the lesson of analytical geometry for traditional education is considered.Based on the results of the study, conclusions were drawn about the benefits and effectiveness of gamification in the classroom. Prospects for the development of the research topic and directions for further activities are outlined. Scientific work can be useful for programmers developing e-Iearning tools.
Keywords: gamijicationt;game mechanics;destructive strategy;activity cycles
Review score: Moderate
Review comment: the article uses last year's template
Title: The Significance of Using Gamification Elements in the Educational Process
Abstract: Gamification elements are used in various fields. Game technologies can also be used in the educational process. How to make lessons interesting with the help of gaming technologies? The article provides an answer to this question. A geometry game was developed and the game mechanics were analyzed. Several solutions have been proposed to combat the destructive strategy. The main constructs of gamification are shown, such as intrinsic and extrinsic motivation, attitude, self-efficacy. On the basis of the developed game, experiments were carried out with students. On the basis of the experiment, the effectiveness of using gamification for the formation of sustainable internal motivation of students to systematically solve problems is shown. Examples are given that illustrate the content aspect of the presented model - fragments of author's developments. The application of the developed game as a material for repeating the topic for the lesson of analytical geometry for traditional education is considered.Based on the results of the study, conclusions were drawn about the benefits and effectiveness of gamification in the classroom. Prospects for the development of the research topic and directions for further activities are outlined. Scientific work can be useful for programmers developing e-Iearning tools.
Keywords: gamijicationt;game mechanics;destructive strategy;activity cycles
Review score: Moderate
Review comment: The proposed paper has good practical implementation. The theoretical side of the article has good foundation. The paper is associated with topics of the conference. On the other hand, it is recommended to provide some of the mathematical foundation in the paper. The form of the list in the paper should be revised. The process of obtaining the results and the processing of them should be presented. Keywords are too common, therefore authors should add more of the Keywords on their theme. The literature list must be formed in the proper way (IEEE Standard).
Title: Research on Creating a Data Warehouse Based on E-Commerce
Abstract: With the popularization of the internet and the rapid development of science and technology, “online shopping” has become the norm in people's lives, and the e-commerce industry is booming, n addition, it has led to an increase in logistics. in today's business Wars, many companies strive for better development in enterprises of the same type, which continue to improve their information capabilities and level. This paper in order to solve the problems such as the increasing of massive data of e-commerce logistics and the phenomenon of data isolation in various business systems. The overall data warehouse is designed and constructed on the Hadoop cluster environment and data warehouse tool Hive is used to process data. Extraction of data from ETL, Sqoop and Flume tools is used for retrieving business data and log data and other aspects of ETL, we use Scala and Java to easily process and filter data and upload it to HDFS. The data warehouse is divided into levels and subject areas to simplify data management. Under the design of the entire system and data warehouse architecture, the conceptual, logical, and physical models of the data warehouse are developed and the star model is selected as a dimensional model. Finally, the application and implementation of data warehouse based on e-commerce logistics will be demonstrated. The development of a data warehouse based on e-commerce logistics not only ensures that e-commerce companies receive logistics information in a timely manner, but also forces decision makers to adjust logistics strategies in a timely manner based on data information, which can also improve user satisfaction and experience, and reduce costs.
Keywords: Electrical business logistics;Data warehouse;The ETL;Hadoop
Title: Application of Deep Learning in the Problem of Image Restoration
Abstract: In recent years, deep learning has become a popular approach for image restoration tasks such as de-noising, super-resolution, and inpainting. In this paper, the current state of the art in applying deep learning techniques to solve these problems was reviewed. In this paper various network architectures and loss functions used in the field are covered, and their strengths and limitations are analyzed. Also, an overview of the evaluation metrics used to assess the performance of these algorithms is provided. Furthermore, several real-world applications were discussed and how deep learning methods have been successfully used to improve the quality of degraded images was demonstrated. Overall, this paper provides a comprehensive survey of the application of deep learning in the field of image restoration and highlights the potential for future research and development in this area.
Keywords: Deep learning;image restoration;Convolutional neural networks (CNNs);Generative adversarial networks (GANs);Image super-resolution;Computer vision
Review score: Moderate
Review comment: In this paper, the authors present a comprehensive overview of the application of deep learning in the field of image restoration and highlight the potential for future research and development in this area.
The value of the article is that the authors reviewed the current state of the art in applying deep learning methods to solve these problems, such as image restoration, noise removal, ultra-high resolution and shading.
This article reviews the various network architectures and loss functions used in the field and analyzes their strengths and limitations.
The result demonstrates the use of several real applications to demonstrate the quality of using deep learning methods to improve degraded images. Since the purpose of this article is to provide an overview of the application of deep learning in the field of image recovery, and the results show a comparative analysis of existing applications, I can agree to publish this article.
Title: Application of Deep Learning in the Problem of Image Restoration
Abstract: In recent years, deep learning has become a popular approach for image restoration tasks such as de-noising, super-resolution, and inpainting. In this paper, the current state of the art in applying deep learning techniques to solve these problems was reviewed. In this paper various network architectures and loss functions used in the field are covered, and their strengths and limitations are analyzed. Also, an overview of the evaluation metrics used to assess the performance of these algorithms is provided. Furthermore, several real-world applications were discussed and how deep learning methods have been successfully used to improve the quality of degraded images was demonstrated. Overall, this paper provides a comprehensive survey of the application of deep learning in the field of image restoration and highlights the potential for future research and development in this area.
Keywords: Deep learning;image restoration;Convolutional neural networks (CNNs);Generative adversarial networks (GANs);Image super-resolution;Computer vision
Review score: Moderate
Review comment: The paper's strength lies in its detailed analysis of the strengths and limitations of different deep learning techniques and evaluation metrics used to assess their performance. The authors' efforts to demonstrate real-world applications of these methods and how they have been successful in improving the quality of degraded images make this paper even more impactful. This research is particularly hopeful because it highlights the potential for future development and research in the field of image restoration. As the paper points out, the use of deep learning in image restoration tasks is still in its infancy, and there is still much to be explored in this area. The paper's authors have paved the way for future researchers to build upon their work and expand upon the existing knowledge.
Title: Improving the Classification Result of Rice Varieties Using Gradient Boosting Methods
Abstract: An accurate identification of rice grain is crucial for classifying rice varieties. This study classifies five distinct rice types that share morphological characteristics using four different machine learning methods. A total of seventy-five thousand records, consisting of fifteen thousand for each variety of rice grains, are collected from previous research. Machine learning methods that are used in this study are the Gradient Boosting method and its variances. The experimental results show that Light Gradient Boosting Machine was the algorithm with the most significant classification success rate compared to other methods, with an accuracy of 98,14%.
Keywords: rice;crops;machine learning;classification;Gradient Boosting method
Review score: Moderate
Review comment: The paper is well organized and well balanced. All experiments are well described and verified. The work has practical value. I heartily recommend this article without any reservations.
Title: Improving the Classification Result of Rice Varieties Using Gradient Boosting Methods
Abstract: An accurate identification of rice grain is crucial for classifying rice varieties. This study classifies five distinct rice types that share morphological characteristics using four different machine learning methods. A total of seventy-five thousand records, consisting of fifteen thousand for each variety of rice grains, are collected from previous research. Machine learning methods that are used in this study are the Gradient Boosting method and its variances. The experimental results show that Light Gradient Boosting Machine was the algorithm with the most significant classification success rate compared to other methods, with an accuracy of 98,14%.
Keywords: rice;crops;machine learning;classification;Gradient Boosting method
Review score: Moderate
Review comment: Good experimental results -Light Gradient Boosting Machine with  significant classification success rate  and with an accuracy of 98,14%
Title: Information and Intelligent Forecasting Systems Based on the Methods of Neural Network Theory
Abstract: This paper is dedicated to the problem of the research of the information and intelligent forecasting systems which solve the problems of regression analysis. This class of the systems includes the systems that use different mathematical apparatus for forecasting, for the implementation of which a wide range of methods and algorithms is used. The paper also deals with the problems of regression analysis using elements of neural network theory. Developing a set of functions to form an approximating function allows you to create models that work efficiently. In existing information and intelligence systems, either implemented functions adapted to solve specific forecasting tasks, or, as in the case of neuropackages, it is considered that the proposed functions can be used to solve a wide range of tasks, adaptation to the conditions of the task occurs during the design of the neural network architecture. To create an approximating function, it is possible to implement linear and non-linear functions, which have proven themselves well in neural network analysis for solving a wide range of tasks and are obtained within the framework of deterministic chaos theory. To implement the function that approximates the dependence of the predicted value on the forecast arguments, the use of a two-layer heterogeneous, artificial neural network without feedback is proposed.
Keywords: information system;intelligent system;forecasting;neural network;regression analysis
Review score: Moderate
Review comment: Generally It is good papper.
Title: Information and Intelligent Forecasting Systems Based on the Methods of Neural Network Theory
Abstract: This paper is dedicated to the problem of the research of the information and intelligent forecasting systems which solve the problems of regression analysis. This class of the systems includes the systems that use different mathematical apparatus for forecasting, for the implementation of which a wide range of methods and algorithms is used. The paper also deals with the problems of regression analysis using elements of neural network theory. Developing a set of functions to form an approximating function allows you to create models that work efficiently. In existing information and intelligence systems, either implemented functions adapted to solve specific forecasting tasks, or, as in the case of neuropackages, it is considered that the proposed functions can be used to solve a wide range of tasks, adaptation to the conditions of the task occurs during the design of the neural network architecture. To create an approximating function, it is possible to implement linear and non-linear functions, which have proven themselves well in neural network analysis for solving a wide range of tasks and are obtained within the framework of deterministic chaos theory. To implement the function that approximates the dependence of the predicted value on the forecast arguments, the use of a two-layer heterogeneous, artificial neural network without feedback is proposed.
Keywords: information system;intelligent system;forecasting;neural network;regression analysis
Review score: High
Review comment: The research to the scope of the paper is relevance of conference. The paper contains of a set of functions provided for the formation of a function that approximates the dependence of the forecast value on the forecast arguments made it possible to create the most effective models.
Accept with minor following improvements: to supplement the abstract of the paper, to include the main directions of the obtained scientific results (linear function; nonlinear functions; nonlinear functions obtained within the framework of the deterministic chaos theory).
Title: Construction Site Modeling Objects Using Artificial Intelligence and BIM Technology: A Multi-Stage Approach
Abstract: This study presents a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an “evolutionary” digital twin. The integration of building information modeling (BIM) and artificial intelligence is used to achieve this goal, with the use of photo modeling using moving cameras and the potential integration of IoT technologies also discussed. A comprehensive artificial intelligence system, combining Convolutional Neural Network (CNN) and Feed Forward Neural Network (FFNN) architectures, has been developed to detect, categorize, and evaluate BIM projects throughout their life cycle. The scalability prospects for point cloud and mesh models, as well as the use of big data technology to optimize the representation of the digital twin, are also addressed. The study determines the effectiveness of construction site conformance detection during the construction of a BIM model, providing consistency and a quantitative evaluation of the processes taking place on the construction site. The findings of this research can be used to enhance BIM modeling methods and concepts toward a multi-stage representation of the digital twin of the construction site.
Keywords: BIM technology;Artificial Intelligence;YOLOv5 model;Construction site;Digital twin
Review score: Moderate
Review comment: The authors presented an interesting research. Comments are only on the design according to the conference template. For example, all formulas in the article as pictures need to be retyped in the formula editor
Title: Construction Site Modeling Objects Using Artificial Intelligence and BIM Technology: A Multi-Stage Approach
Abstract: This study presents a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an “evolutionary” digital twin. The integration of building information modeling (BIM) and artificial intelligence is used to achieve this goal, with the use of photo modeling using moving cameras and the potential integration of IoT technologies also discussed. A comprehensive artificial intelligence system, combining Convolutional Neural Network (CNN) and Feed Forward Neural Network (FFNN) architectures, has been developed to detect, categorize, and evaluate BIM projects throughout their life cycle. The scalability prospects for point cloud and mesh models, as well as the use of big data technology to optimize the representation of the digital twin, are also addressed. The study determines the effectiveness of construction site conformance detection during the construction of a BIM model, providing consistency and a quantitative evaluation of the processes taking place on the construction site. The findings of this research can be used to enhance BIM modeling methods and concepts toward a multi-stage representation of the digital twin of the construction site.
Keywords: BIM technology;Artificial Intelligence;YOLOv5 model;Construction site;Digital twin
Review score: Moderate
Review comment: The paper describes a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an “evolutionary” digital twin.

In general, the procedure is quite clear. However, the manuscript has one critical problem, as follows. The literature review provided in the introduction does not allow the reader to clearly understand the contribution of the paper with respect to the previous literature. That is why,I think  it is difficult to understand if the paper contribution is significant. 

The overall organization of the manuscript could be improved. The screenshots of the formulae are not acceptable in paper submission. Tables are out of bounds.
Title: Projects of Digital Transformation of Shipbuilding Enterprises
Abstract: The problem of digital transformation of Ukrainian shipbuilding enterprises is considered. On the basis of the performed analysis, the directions of digitalization are determined. It is shown that the further development of the shipbuilding industry is planned to be carried out on the basis of the Shipbuilding 4.0 technological platform. The analysis which was fellfield shows the absence of models for managing digital transformation projects of regional shipbuilding enterprises. The basic principles for building models for managing digital transformation projects have been developed, and probable projects have been identified.
Keywords: industry 4.0;shipbuilding;management models;principles;projects
Review score: Moderate
Review comment: The scientific argument is logical and persuasive. 
The topic is scientifically relevant and useful. 
Conclusions must be improved. 
There are no quantitative results of the research.
There is no complete overview of known, prior solutions. 
Poor list of reference
The format rules of paper have not been observed 
the list of variables should be preceded by is /are, not a dash.
Title: Projects of Digital Transformation of Shipbuilding Enterprises
Abstract: The problem of digital transformation of Ukrainian shipbuilding enterprises is considered. On the basis of the performed analysis, the directions of digitalization are determined. It is shown that the further development of the shipbuilding industry is planned to be carried out on the basis of the Shipbuilding 4.0 technological platform. The analysis which was fellfield shows the absence of models for managing digital transformation projects of regional shipbuilding enterprises. The basic principles for building models for managing digital transformation projects have been developed, and probable projects have been identified.
Keywords: industry 4.0;shipbuilding;management models;principles;projects
Review score: High
Review comment: Present there are no methods, models and mechanisms for managing digital transformation projects of enterprises in the shipbuilding industry, which makes the task of their development relevant.The basic principles for building models for managing digital transformation projects have been developed, and probable projects have been identified. There is a remark on the quality of figure 1, it is necessary to replace it. After correction, comments can be submitted for publication.
Title: The Possibilities of Using Fractal Antennas in Modern Wireless Communication Technologies
Abstract: This article discusses the possibilities of using fractal antenna systems in wireless communication systems, their features and the main advantages of using them. The presence of the property of self-similarity of fractal structures allows them to be implemented in the creation of broadband and multiband antennas. Therefore, today the capabilities of a fractal antenna are widely studied in wireless communication systems such as 5G, 4G (LTE), Bluetooth, WLAN, WiMAX, GPS and others. The article also presents the main characteristics of fractal antennas and the practical implementation of the antenna model in the Ansys HFSS program. Simulation results, such as S11-parameter, radiation pattern and gain for MIMO patch antenna based on anisotropic fractal, and L, S band patch antenna for CubeSat nanosatellites are presented.
Keywords: fractal antenna;antenna array;HFSS;MIMO;anisotropic fractal;gain;S11;CubeSat
Review score: Moderate
Review comment: accept
Title: The Possibilities of Using Fractal Antennas in Modern Wireless Communication Technologies
Abstract: This article discusses the possibilities of using fractal antenna systems in wireless communication systems, their features and the main advantages of using them. The presence of the property of self-similarity of fractal structures allows them to be implemented in the creation of broadband and multiband antennas. Therefore, today the capabilities of a fractal antenna are widely studied in wireless communication systems such as 5G, 4G (LTE), Bluetooth, WLAN, WiMAX, GPS and others. The article also presents the main characteristics of fractal antennas and the practical implementation of the antenna model in the Ansys HFSS program. Simulation results, such as S11-parameter, radiation pattern and gain for MIMO patch antenna based on anisotropic fractal, and L, S band patch antenna for CubeSat nanosatellites are presented.
Keywords: fractal antenna;antenna array;HFSS;MIMO;anisotropic fractal;gain;S11;CubeSat
Review score: Moderate
Review comment: Generally It is good papper.