Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Development of an Intelligent Video Surveillance System Based on YOLO Algorithm
Abstract: In the contemporary landscape characterized by swift technological advancements and escalating security challenges, the evolution of intelligent video surveillance systems emerges as a pivotal innovation. These systems, integral to the security of both public and private domains, harness the power of automated, advanced analytics to enhance operational efficiency and situational awareness without necessitating constant human oversight. This paper focuses on the development of an intelligent video surveillance system tailored specifically for office premises, emphasizing the system’s architecture and the strategic selection of technologies. Through meticulous exploration, this study evaluates state-of-the-art models for real-time object detection tasks, acknowledging the intricate balance between performance and accuracy essential for deploying such algorithms. The proposed framework includes a comprehensive comparison of various models, culminating in the design of a robust system architecture and a web-based infrastructure for seamless implementation. The system’s effectiveness is demonstrated through experimental results, showcasing its capability in real-world scenarios such as occupancy monitoring, boundary control, and facial recognition. This paper contributes to the field by showcasing the development and implementation of a sophisticated intelligent video surveillance system, highlighting its potential in enhancing security and operational efficiency in office environments.
Keywords: YOLO;deep neural networks;intelligent video surveillance;computer vision;real-time object detection
Review score: High
Review comment: The article successfully demonstrates the potential of using the YOLO v8 algorithm for developing an intelligent video surveillance system tailored to office environments. It provides a solid foundation for future research and development in the field of intelligent surveillance. The inclusion of real-time object detection and a suite of related security modules presents a significant contribution to enhancing operational efficiency and security management in office settings. While there are areas for further exploration and enhancement, particularly regarding scalability, privacy, and integration with broader security systems, the study marks a significant step forward in the practical application of advanced computer vision algorithms for surveillance purposes.
Title: Development of an Intelligent Video Surveillance System Based on YOLO Algorithm
Abstract: In the contemporary landscape characterized by swift technological advancements and escalating security challenges, the evolution of intelligent video surveillance systems emerges as a pivotal innovation. These systems, integral to the security of both public and private domains, harness the power of automated, advanced analytics to enhance operational efficiency and situational awareness without necessitating constant human oversight. This paper focuses on the development of an intelligent video surveillance system tailored specifically for office premises, emphasizing the system’s architecture and the strategic selection of technologies. Through meticulous exploration, this study evaluates state-of-the-art models for real-time object detection tasks, acknowledging the intricate balance between performance and accuracy essential for deploying such algorithms. The proposed framework includes a comprehensive comparison of various models, culminating in the design of a robust system architecture and a web-based infrastructure for seamless implementation. The system’s effectiveness is demonstrated through experimental results, showcasing its capability in real-world scenarios such as occupancy monitoring, boundary control, and facial recognition. This paper contributes to the field by showcasing the development and implementation of a sophisticated intelligent video surveillance system, highlighting its potential in enhancing security and operational efficiency in office environments.
Keywords: YOLO;deep neural networks;intelligent video surveillance;computer vision;real-time object detection
Review score: Moderate
Review comment: Well, the paper is really good and it's written in a way that's easy to understand and engaging. It definitely deserves to be presented at the conference.
Title: Development of a database for the main stationary sources of pollution with regulation of emissions quotas
Abstract: The article discusses one of the key priorities of Industry 4.0 – the development of a smart industry. In this context, special attention is paid to environmental sustainability and improving the environmental situation. The article considers atmospheric air quality monitoring as an important tool for sustainable development management. Monitoring is needed to assess emissions over time, making it possible to determine the effectiveness of measures to reduce emissions and make adjustments in time. A brief overview of existing environmental monitoring systems and their main limitations was also conducted. On the basis of this, it was concluded that an intelligent environmental information and analytical monitoring system with integrated blockchain technology is necessary to be developed.The authors propose the conceptual integration of blockchain and IoT technology in an intelligent system.A database of the main stationary sources of pollutants, with regulation of emission quotas, has been designed and developed as one of the key components in the intelligent system.The results of this study will provide an important contribution to the fields of environmental monitoring and air quality management.
Keywords: emission of harmful substances;regulation of emission quotas;modern data structure;regulatory and control mechanisms;blockchain technologies;smart contracts;high level of security;intelligent approach;environmental problems
Review score: Moderate
Review comment: The article is devoted to the development of a database on the main stationary sources of pollution with the regulation of emission quotas. This topic is relevant for many industrial regions of Kazakhstan. The article in particular examines atmospheric air pollution. The article was written according to the requirements for scientific articles. However, there are several disadvantages:
1) A comparative analysis of the DBMS for implementing the database is not provided, and it is also not described on which DBMS the implementation was carried out.
2) A conceptual database model describing the relationship between objects and their characteristics is not provided.
3) There is no computational experiment of a mathematical model of the process of accumulating database information.
Title: Development of a database for the main stationary sources of pollution with regulation of emissions quotas
Abstract: The article discusses one of the key priorities of Industry 4.0 – the development of a smart industry. In this context, special attention is paid to environmental sustainability and improving the environmental situation. The article considers atmospheric air quality monitoring as an important tool for sustainable development management. Monitoring is needed to assess emissions over time, making it possible to determine the effectiveness of measures to reduce emissions and make adjustments in time. A brief overview of existing environmental monitoring systems and their main limitations was also conducted. On the basis of this, it was concluded that an intelligent environmental information and analytical monitoring system with integrated blockchain technology is necessary to be developed.The authors propose the conceptual integration of blockchain and IoT technology in an intelligent system.A database of the main stationary sources of pollutants, with regulation of emission quotas, has been designed and developed as one of the key components in the intelligent system.The results of this study will provide an important contribution to the fields of environmental monitoring and air quality management.
Keywords: emission of harmful substances;regulation of emission quotas;modern data structure;regulatory and control mechanisms;blockchain technologies;smart contracts;high level of security;intelligent approach;environmental problems
Review score: High
Review comment: 1. It is necessary to number the formulas according to the requirements (page 5)
2. The images are not clear
3. Complete a Conclusion
Title: Development and Research of Phased Array Antenna Control Unit for Educational Purposes
Abstract: The research examined the technology of creating one of the main component of the phased array antenna - the phase shifter control unit. This control unit is designed to operate a phased array antenna model of four emitters for academic purposes. The antenna model includes four emitters, in the circuit of each emitter there are two switches that form the necessary phase shifts. Switches can be changed over in three directions, which allows the formation of three antenna beams. The control unit sends commands to each switch to select one of three possible states, which allows to form the required phase distribution on the emitters. To create a control unit, the method of printed circuits was used, and the program "Sprint-Layout 6" was used for the wiring of printed circuit boards. The relevance of using this method to reduce the size, weight and cost of the antenna system nodes is also noted, which is an important factor in the design of antennas
Keywords: control unit;phased antenna array;phase shifte;p-i-n diode;switch
Review score: Moderate
Review comment: It is necessary to describe the educational purposes in detail
Title: Development and Research of Phased Array Antenna Control Unit for Educational Purposes
Abstract: The research examined the technology of creating one of the main component of the phased array antenna - the phase shifter control unit. This control unit is designed to operate a phased array antenna model of four emitters for academic purposes. The antenna model includes four emitters, in the circuit of each emitter there are two switches that form the necessary phase shifts. Switches can be changed over in three directions, which allows the formation of three antenna beams. The control unit sends commands to each switch to select one of three possible states, which allows to form the required phase distribution on the emitters. To create a control unit, the method of printed circuits was used, and the program "Sprint-Layout 6" was used for the wiring of printed circuit boards. The relevance of using this method to reduce the size, weight and cost of the antenna system nodes is also noted, which is an important factor in the design of antennas
Keywords: control unit;phased antenna array;phase shifte;p-i-n diode;switch
Review score: Moderate
Review comment: I recommend drawing pictures and labeling them with numbers. In particular, this is the figure - 5, 6, 7.
Title: Systemic approach to optimizing natural language processing technologies in Astana IT University's admissions process
Abstract: Impelemtation of artificial intelligence (AI) has a transformative potential for various sectors, including higher education. This study is focused on an AI system's effective development and accuracy in streamlining university admissions. The focal point is the AITU Admissions Advisor, an AI solution crafted to navigate the complexities of the admissions process. The study examines the problem of operational inefficiencies and inaccuracies that plague traditional admissions methods, and it positions the AI system as a remedy by offering automation and intelligent decision-making capabilities. The essence of the findings derives from a methodical evaluation of the AITU system against conventional practices, revealing its enhanced efficiency and precision in handling admissions procedures. Distinguishing features of these results include the system's adept use of natural language processing (NLP), sophisticated machine learning models, and a dynamic feedback system that collectively elevate its performance metrics. These technological strides underscore the system's reliability and responsiveness to the nuanced needs of applicants and administrators alike. The paper concludes that for practical implementation, seamless integration with existing university infrastructures, thorough staff training, and continuous system monitoring are imperative. This study provides a blueprint for the application of AI in higher education, showcasing a system that not only meets but anticipates the demands of modern university admissions.
Keywords: Artificial Intelligence Efficiency;Admissions Process Automation;NLP Implementation;EdTech Advances
Review score: High
Review comment: The study addresses inefficiencies in traditional admissions methods and presents the AI system as a solution through automation and intelligent decision-making. Results showcase the AITU system's enhanced efficiency and precision, attributed to natural language processing, advanced machine learning models, and a dynamic feedback system. These technological advancements highlight the system's reliability and responsiveness to varied user needs. The paper emphasizes the importance of seamless integration with university infrastructures, staff training, and ongoing system monitoring for practical implementation. Overall, the study serves as a blueprint for AI application in higher education, demonstrating a system that anticipates and meets modern university admission demands.

In general, the article meets all the requirements of the conference and can be accepted for publication.
Title: Systemic approach to optimizing natural language processing technologies in Astana IT University's admissions process
Abstract: Impelemtation of artificial intelligence (AI) has a transformative potential for various sectors, including higher education. This study is focused on an AI system's effective development and accuracy in streamlining university admissions. The focal point is the AITU Admissions Advisor, an AI solution crafted to navigate the complexities of the admissions process. The study examines the problem of operational inefficiencies and inaccuracies that plague traditional admissions methods, and it positions the AI system as a remedy by offering automation and intelligent decision-making capabilities. The essence of the findings derives from a methodical evaluation of the AITU system against conventional practices, revealing its enhanced efficiency and precision in handling admissions procedures. Distinguishing features of these results include the system's adept use of natural language processing (NLP), sophisticated machine learning models, and a dynamic feedback system that collectively elevate its performance metrics. These technological strides underscore the system's reliability and responsiveness to the nuanced needs of applicants and administrators alike. The paper concludes that for practical implementation, seamless integration with existing university infrastructures, thorough staff training, and continuous system monitoring are imperative. This study provides a blueprint for the application of AI in higher education, showcasing a system that not only meets but anticipates the demands of modern university admissions.
Keywords: Artificial Intelligence Efficiency;Admissions Process Automation;NLP Implementation;EdTech Advances
Review score: Moderate
Review comment: The work presents interesting research results, but scientific mathematical models and modeling methods are not given
Title: Community structure detection in complex network based on MST and modularity
Abstract: This research introduces a new splitting and merging method to identify community structure in networks. This proposed approach relies on constructing a minimal spanning tree (MST) based on the dissimilarities between nodes in a graph and optimizing the modularity function. The Minimum Spanning Tree (MST) of a graph is detached by deleting edges with high dissimilarity values between nodes during the splitting phase. The method identifies the initial community structure after completing this stage. The subsequent stage involves a merging procedure. Communities are progressively combined to identify the optimal community structure with a high modularity value. This proposed approach is parameterless. This study offers a comprehensive structure for executing this strategy. The suggested method was tested on computer-generated networks and several real-world networks, demonstrating its usefulness through experimental results.
Keywords: MST;Networks;Modularity;Community Structure;NMI
Review score: High
Review comment: 1. Please, avoid the words like "we", "our", etc.
2. It is need to use the writing format from third party side.
3. The chapter about Methodology is need to rename and to write as a Methodology or Methods and Approaches.
Title: Community structure detection in complex network based on MST and modularity
Abstract: This research introduces a new splitting and merging method to identify community structure in networks. This proposed approach relies on constructing a minimal spanning tree (MST) based on the dissimilarities between nodes in a graph and optimizing the modularity function. The Minimum Spanning Tree (MST) of a graph is detached by deleting edges with high dissimilarity values between nodes during the splitting phase. The method identifies the initial community structure after completing this stage. The subsequent stage involves a merging procedure. Communities are progressively combined to identify the optimal community structure with a high modularity value. This proposed approach is parameterless. This study offers a comprehensive structure for executing this strategy. The suggested method was tested on computer-generated networks and several real-world networks, demonstrating its usefulness through experimental results.
Keywords: MST;Networks;Modularity;Community Structure;NMI
Review score: Moderate
Review comment: 1. References [3], [5], [18], [19], [20], [21] are not used in the paper.
2. Please revise the format of the references according to the template format.
Title: Analysis of Machine Learning Algorithms for Forecasting Harvest Data in Kazakhstan
Abstract: This study presents a comparative analysis of machine learning algorithms for forecasting harvest data in Kazakhstan. Leveraging extensive datasets spanning over three decades, our research evaluates the performance and predictive accuracy of various algorithms in forecasting potato yields—the predominant crop in the region. Through empirical analysis, Linear Regression, Lasso Regression, and Ridge Regression emerge as top-performing models, exhibiting R^2 scores exceeding 0.95. These linear models effectively capture the complex relationship between weather conditions and agricultural outcomes, offering valuable insights for agricultural planning and decision-making. Additionally, Gradient Boosting and ElasticNet algorithms demonstrate competitive performance, highlighting the potential of ensemble learning techniques in agricultural forecasting. Conversely, Support Vector Regression (SVR) exhibits poor performance in this context, emphasizing the importance of selecting appropriate algorithms tailored to the specific characteristics of agricultural datasets. Overall, our findings underscore the significance of employing advanced machine learning techniques to enhance the accuracy and reliability of harvest forecasts, thereby empowering stakeholders in the agricultural sector to make informed decisions and ensure food security in Kazakhstan.
Keywords: machine learning;agricultural forecasting;harvest data;Kazakhstan;predictive accuracy
Title: Development of a prototype of fiber-optic health monitoring of the system of extended reinforced concrete facilities
Abstract: This article demonstrates the research results on developing a prototype fiber-optic health monitoring system for extended reinforced concrete infrastructure facilities. These facilities include buildings and structures and other long structures such as bridges, tunnels, and power lines. The basic concept of the study is based on the use of single-mode fiber as a critical element of the sensor, which is integrated into the structure of extended elements such as beams, columns, and plates, thereby allowing continuous and accurate monitoring of their condition. The process of the calculation of the load is based on the amount of the white pixels of the light spot`s negative image. The mathematical description presented in article explains the process of the whole system. Practical implementation of the system is presented as hardware-software complex. The results of work is presented as graphs and light spot negative images.
Keywords: Monitoring System;Fiber Optics;Reinforced Concrete Structures;Optical Sensor;Destruction Monitoring
Review score: High
Review comment: This work is good in terms of novelty and relavance and worth to be published. But I recommend major revision. Here are my suggestions for improvement:
1. abstract section should be extended to be more comprehensive for a general reader
2. Grammar should be corrected
3. Introduction and literature review should be merged
4. Results and Discussion sections are lacking mainly methods section included
5. a detailed explanation of the experimental setup and procedure should be included.
6. Clarify the experimental conditions, including environmental factors, load conditions, and the duration of monitoring, to understand the context and applicability of the findings.
7. While the manuscript presents a mathematical model, a deeper statistical analysis demonstrating the reliability, sensitivity, and accuracy of the monitoring system is necessary. 
8. The literature review identifies limitations of current monitoring methods but does not provide a direct comparison of the proposed system’s efficacy against these. I suggest to include it in the discussion section
9. While the manuscript touches on future research directions, a more detailed exploration is needed. 
10. The manuscript has some formatting inconsistencies and typographical errors. Should be edited according to SIST editorial requirements.
Title: Development of a prototype of fiber-optic health monitoring of the system of extended reinforced concrete facilities
Abstract: This article demonstrates the research results on developing a prototype fiber-optic health monitoring system for extended reinforced concrete infrastructure facilities. These facilities include buildings and structures and other long structures such as bridges, tunnels, and power lines. The basic concept of the study is based on the use of single-mode fiber as a critical element of the sensor, which is integrated into the structure of extended elements such as beams, columns, and plates, thereby allowing continuous and accurate monitoring of their condition. The process of the calculation of the load is based on the amount of the white pixels of the light spot`s negative image. The mathematical description presented in article explains the process of the whole system. Practical implementation of the system is presented as hardware-software complex. The results of work is presented as graphs and light spot negative images.
Keywords: Monitoring System;Fiber Optics;Reinforced Concrete Structures;Optical Sensor;Destruction Monitoring
Review score: Moderate
Review comment: The developed prototype offers promising results, showcasing its potential for real-time monitoring of reinforced concrete structures. By leveraging fiber-optic technology, the system addresses the limitations of traditional monitoring methods and provides a cost-effective and efficient solution for infrastructure health management. Further validation and refinement of the prototype could lead to its widespread adoption in the construction industry.