Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Comparative effectiveness of models and methods for diagnosing diseases of internal organs
Abstract: Among the various applications of IT in healthcare, automated systems for diagnosing diseases stand out for their utilization of mathematical models in medical decision-making. These systems, capable of analyzing extensive datasets, offer a level of speed and accuracy in diagnosing diseases of internal organs that often surpasses human capability. This paper aims to elucidate and validate the effectiveness of these automated systems through a comprehensive approach involving comparative studies and clinical trials. By examining the accuracy of different mathematical models and highlighting the percentage of cases correctly identified by expert systems, this research underscores the systems' proficiency in rapid data processing and analysis. Drawing on previous research, the complexity of medical diagnostics is explored through the diversity of diagnostic indicators and the development of robust mathematical models based on fuzzy set theory and gradient projection techniques. The paper presents a scheme of the automated diagnostic system, detailing its core components and the innovative use of case-based reasoning. Through a retrospective analysis and clinical trial outcomes, the effectiveness of the proposed system is demonstrated, showcasing its significant potential to enhance diagnostic accuracy and efficiency in the medical field.
Keywords: diagnosing diseases;automated diagnostic systems;clinical trials;fuzzy set theory;gradient projection techniques;diagnostic accuracy;data analysis;diagnostic efficiency
Review score: Moderate
Review comment: The paper provides a comprehensive review of automated systems for disease diagnosis in healthcare, focusing on their utilization of mathematical models in medical decision-making. It explores the effectiveness of these systems through comparative studies and clinical trials, emphasizing their ability to analyze extensive datasets and surpass human capability in diagnosing diseases of internal organs. By examining the accuracy of different mathematical models and expert systems, the research underscores the proficiency of these systems in rapid data processing and analysis.
The complexity of medical diagnostics is discussed, highlighting the diversity of diagnostic indicators and the development of robust mathematical models based on fuzzy set theory and gradient projection techniques. The paper presents a scheme of the automated diagnostic system, detailing its core components and the innovative use of case-based reasoning.
Through retrospective analysis and clinical trial outcomes, the paper demonstrates the effectiveness of the proposed system, showcasing its significant potential to enhance diagnostic accuracy and efficiency in the medical field.
Title: Comparative effectiveness of models and methods for diagnosing diseases of internal organs
Abstract: Among the various applications of IT in healthcare, automated systems for diagnosing diseases stand out for their utilization of mathematical models in medical decision-making. These systems, capable of analyzing extensive datasets, offer a level of speed and accuracy in diagnosing diseases of internal organs that often surpasses human capability. This paper aims to elucidate and validate the effectiveness of these automated systems through a comprehensive approach involving comparative studies and clinical trials. By examining the accuracy of different mathematical models and highlighting the percentage of cases correctly identified by expert systems, this research underscores the systems' proficiency in rapid data processing and analysis. Drawing on previous research, the complexity of medical diagnostics is explored through the diversity of diagnostic indicators and the development of robust mathematical models based on fuzzy set theory and gradient projection techniques. The paper presents a scheme of the automated diagnostic system, detailing its core components and the innovative use of case-based reasoning. Through a retrospective analysis and clinical trial outcomes, the effectiveness of the proposed system is demonstrated, showcasing its significant potential to enhance diagnostic accuracy and efficiency in the medical field.
Keywords: diagnosing diseases;automated diagnostic systems;clinical trials;fuzzy set theory;gradient projection techniques;diagnostic accuracy;data analysis;diagnostic efficiency
Review score: Moderate
Review comment: The article is interesting, but has many typos and inaccuracies. The article must comply with IEEE requirements.
Title: Logical Sum of NDWI & NDPI Data for Detecting Flooded Houses: A Case of Atbasar Town, 2017
Abstract: In this article, NDWI and NDPI data were considered to detect flooded houses hit by a spring flood in Atbasar town, in 2017. This study used remote sensing data Sentinel-2A and Landsat-8 to compute water radiometric indices and formation binary images. Binary images are flood maps generated by threshold values from NDWI and NDPI data and comprise two classes (legend): water body (black color); non-waterbody (white color). Various water indices data were visually analyzed, and the choice was made to Normalized Difference Water Index (NDWI) and Normalized Difference Pond Index (NDPI) as better. NDWI and NDPI data of Sentinel-2A and Landsat-8 were fused by logical summation. After that, this result was imposed on GIS data of Atbasar Town to define locations and number of flooded houses.
Keywords: NDWI;NDPI;logical sum;Sentinel-2;Landsat-8;flood
Review score: Low
Review comment: In this paper, the authors propose to combine images from two satellite systems (Sentinel-2A and Landsat-8) together with NDWI and NDPI data for detecting flooded houses. The paper requires a clear problem statement indicating state-of-the-art methods and their drawbacks that authors are trying to solve/address. The scientific novelty of the presented work is not well-explained. A comparison with alternative methods for benchmarking is required to highlight advantages of the used methodology. A major revision is required.
Title: Logical Sum of NDWI & NDPI Data for Detecting Flooded Houses: A Case of Atbasar Town, 2017
Abstract: In this article, NDWI and NDPI data were considered to detect flooded houses hit by a spring flood in Atbasar town, in 2017. This study used remote sensing data Sentinel-2A and Landsat-8 to compute water radiometric indices and formation binary images. Binary images are flood maps generated by threshold values from NDWI and NDPI data and comprise two classes (legend): water body (black color); non-waterbody (white color). Various water indices data were visually analyzed, and the choice was made to Normalized Difference Water Index (NDWI) and Normalized Difference Pond Index (NDPI) as better. NDWI and NDPI data of Sentinel-2A and Landsat-8 were fused by logical summation. After that, this result was imposed on GIS data of Atbasar Town to define locations and number of flooded houses.
Keywords: NDWI;NDPI;logical sum;Sentinel-2;Landsat-8;flood
Review score: Moderate
Review comment: This research is proposing a novel logical sum method for water body extraction, where four binary images are used. The work needs major improvements in organization, reporting, and presenting contribution of the paper. The abstract is not clear, and definitely needs the motivation part. All abbreviations should be explained. It is important to note that it is unclear why this proposed method needs research attention, and what is the contribution of this new methodology.  Also, the conclusion says that "It was found that, with the existing optical Earth remote sensing data of Landsat-8 and Sentinel-2, it is impossible to accurately determine the flooding of the eastern part of the town of Atbasar because the spatial resolution of the images is less or comparable to the distance between houses or buildings. Flooding can be identified even in this case if the water level is above the roofs of houses or buildings". Authors should verify all the findings and statements.
Title: Using Artificial Intelligence to Convert Code to Another Programming Language
Abstract: This study presents a solution to the problem of automating the conversion of program code between different programming languages using artificial intelligence (AI) technologies This area is becoming increasingly relevant with the development of multilingual projects and the need to integrate existing solutions written in different programming languages. The difficulty of translating code between programming languages lies not only in the syntactic differences between languages, but also in preserving the semantics and logic of the original code. Artificial intelligence can help to recognize patterns and dependencies in the code, which will allow you to more effectively adapt the code to the target language, taking into account its features. The use of machine learning algorithms, in particular neural networks, allows you to develop systems that can learn from code examples and independently improve their conversion algorithms. Such systems analyze the abstract syntax tree (AST) of the code, create intermediate representations, and generate the target code. Various tools and platforms have been developed to simplify this process and increase its accuracy. The process of code transformation with the help of AI is used in many areas, including refactoring existing software, integrating systems written in different languages, and in the educational process, where it can be used to teach students the basics of different programming languages.
Keywords: Artificial Intelligencet;AI;Neural Networks;abstract syntax tree;Programming Language
Review score: High
Review comment: The article requires more mathemtical backround it can be represented in any kind of mathematics.
Title: Using Artificial Intelligence to Convert Code to Another Programming Language
Abstract: This study presents a solution to the problem of automating the conversion of program code between different programming languages using artificial intelligence (AI) technologies This area is becoming increasingly relevant with the development of multilingual projects and the need to integrate existing solutions written in different programming languages. The difficulty of translating code between programming languages lies not only in the syntactic differences between languages, but also in preserving the semantics and logic of the original code. Artificial intelligence can help to recognize patterns and dependencies in the code, which will allow you to more effectively adapt the code to the target language, taking into account its features. The use of machine learning algorithms, in particular neural networks, allows you to develop systems that can learn from code examples and independently improve their conversion algorithms. Such systems analyze the abstract syntax tree (AST) of the code, create intermediate representations, and generate the target code. Various tools and platforms have been developed to simplify this process and increase its accuracy. The process of code transformation with the help of AI is used in many areas, including refactoring existing software, integrating systems written in different languages, and in the educational process, where it can be used to teach students the basics of different programming languages.
Keywords: Artificial Intelligencet;AI;Neural Networks;abstract syntax tree;Programming Language
Review score: Moderate
Review comment: The mathematical part that underlies the research is missing or poorly represented, it is necessary to add. Improvements are needed in the structure of the manuscript - to more explicitly state the purpose of the study, the tasks, and the results obtained. It is necessary to present the proposed algorithm, as well as to explain the differences from the known ones.
Title: Development of Augmented Reality Game Using Computer Vision Technology
Abstract: This research investigates the development and practical application of an innovative augmented reality (AR) system, empowered by computer vision, designed to enhance children's motivation to engage in physical activities through interactive gaming experiences. The system effectively captures and interprets a diverse range of physical movements, including lateral shifts, crouching, and jumping, seamlessly integrating these actions into the gaming environment. Our findings underscore the potential of this pioneering approach to bridge the gap between sedentary screen-based activities and active physical play, offering a harmonious fusion of electronic gaming and real-world sports. This integration not only serves as a catalyst for children to actively participate in physical exercises but also facilitates the acquisition of essential motor skills, contributing to their overall healthy development. Furthermore, the versatility of this approach extends beyond the realm of children, with potential applications in various contexts involving adults. This research not only propels the concept of gamified physical activity promotion but also holds the promise of reshaping the way individuals of diverse age groups engage with technology to lead healthier lifestyles.
Keywords: Computer vision;augmented reality;game development;image analysis;image processing
Review score: Low
Review comment: This paper aims to investigate the effectiveness of an AR-based sports game in fostering children's participation in physical activities. The authors have developed an application capable of detecting human pose. The authors need to add some quantitative data in the Result section demonstrating performance of their system such as pose estimation accuracy. At this stage, the Results section is compiled out of several game screenshots and the text explaining what can be seen on the figure. The top and bottom black sections on each figure can be removed to improve readability of the text and let readers focus on the more important part of the figures.
Title: Development of Augmented Reality Game Using Computer Vision Technology
Abstract: This research investigates the development and practical application of an innovative augmented reality (AR) system, empowered by computer vision, designed to enhance children's motivation to engage in physical activities through interactive gaming experiences. The system effectively captures and interprets a diverse range of physical movements, including lateral shifts, crouching, and jumping, seamlessly integrating these actions into the gaming environment. Our findings underscore the potential of this pioneering approach to bridge the gap between sedentary screen-based activities and active physical play, offering a harmonious fusion of electronic gaming and real-world sports. This integration not only serves as a catalyst for children to actively participate in physical exercises but also facilitates the acquisition of essential motor skills, contributing to their overall healthy development. Furthermore, the versatility of this approach extends beyond the realm of children, with potential applications in various contexts involving adults. This research not only propels the concept of gamified physical activity promotion but also holds the promise of reshaping the way individuals of diverse age groups engage with technology to lead healthier lifestyles.
Keywords: Computer vision;augmented reality;game development;image analysis;image processing
Review score: Moderate
Review comment: The topic of this article is interesting. The paper well written and the objectives is presented very clearly. However, the authors may revise this article to enhence its quality by considering the following concerns:
1. In Figure 1, the graphic form of the recording (in particular, figures) must comply with the rules for constructing algorithms
2. If figures were borrowed from other literature, it is necessary to provide a link to them.
3. Strong motivation and arguments are required to include to justify the advantages and disadvantages of the proposed method.
4. First author's mail was missed
Title: Development of a Security and Monitoring System for Server Equipment Utilizing IoT Technologies
Abstract: Modern organizations are facing a constant increase in dependency on server equipment, underscoring the importance of ensuring its security and effective monitoring. This article explores the application of Internet of Things (IoT) technologies to create an intelligent security and monitoring system for server equipment. The primary objective is to provide reliable protection and real-time monitoring, as well as oversight of servicing companies' actions. This research extends beyond merely enhancing the security of server equipment to vigilant supervision of servicing companies. This step-by-step investigation will meticulously outline the deployment of a Home Assistant server, integration of various sensors, and the creation of automation processes to maximize the efficiency of the security and monitoring system. With each stage completed, the ultimate goal is to establish a robust framework enabling organizations to actively address potential issues and ensure optimal functioning of their server infrastructure.
Keywords: IoT technologies;Server hardware security;System automation;Home Assistant;Technical maintenance;Intelligent Control Systems
Review score: High
Review comment: - references to literature need to be corrected
- no literature review available
Title: Development of a Security and Monitoring System for Server Equipment Utilizing IoT Technologies
Abstract: Modern organizations are facing a constant increase in dependency on server equipment, underscoring the importance of ensuring its security and effective monitoring. This article explores the application of Internet of Things (IoT) technologies to create an intelligent security and monitoring system for server equipment. The primary objective is to provide reliable protection and real-time monitoring, as well as oversight of servicing companies' actions. This research extends beyond merely enhancing the security of server equipment to vigilant supervision of servicing companies. This step-by-step investigation will meticulously outline the deployment of a Home Assistant server, integration of various sensors, and the creation of automation processes to maximize the efficiency of the security and monitoring system. With each stage completed, the ultimate goal is to establish a robust framework enabling organizations to actively address potential issues and ensure optimal functioning of their server infrastructure.
Keywords: IoT technologies;Server hardware security;System automation;Home Assistant;Technical maintenance;Intelligent Control Systems
Review score: Moderate
Review comment: It is strongly suggested to: 
1. Include more literature on the considering problem 
2. Include Discussion session to discuss the results of the paper.
Title: Possibility of using the Brillouin reflectometry method to determine deformations in bridge structures
Abstract: This article discusses the possibilities of using the Brillouin reflectometry method BOTDR for monitoring the condition of construction bridge structures, their features and the main advantages of use. Timely and reliable monitoring of load parameters allows you to increase the level of reliability of bridge structures, save time on troubleshooting and provide predictive information about existing problem areas, the causes of deformation, weakening of load-bearing structures of bridges, as well as the impact of vehicle loads on these structures, which together can lead to to multi-million dollar costs or, even worse, to tragic consequences. The article also presents the main characteristics of the Brillouin reflectometry method and the practical implementation of the model of bridge structures. Some measurement results using the Brillouin reflectometry method are presented, which makes it possible to promptly eliminate the causes and prevent damage.
Keywords: bridge structures;Brillouin reflectometry;early diagnosis;deformation;weakening;level;spectrogram;optical fibers
Review score: Low
Review comment: The submitted paper does not pass an originality check. Similarity with other works is too high.
Title: Possibility of using the Brillouin reflectometry method to determine deformations in bridge structures
Abstract: This article discusses the possibilities of using the Brillouin reflectometry method BOTDR for monitoring the condition of construction bridge structures, their features and the main advantages of use. Timely and reliable monitoring of load parameters allows you to increase the level of reliability of bridge structures, save time on troubleshooting and provide predictive information about existing problem areas, the causes of deformation, weakening of load-bearing structures of bridges, as well as the impact of vehicle loads on these structures, which together can lead to to multi-million dollar costs or, even worse, to tragic consequences. The article also presents the main characteristics of the Brillouin reflectometry method and the practical implementation of the model of bridge structures. Some measurement results using the Brillouin reflectometry method are presented, which makes it possible to promptly eliminate the causes and prevent damage.
Keywords: bridge structures;Brillouin reflectometry;early diagnosis;deformation;weakening;level;spectrogram;optical fibers
Review score: Moderate
Review comment: The topic of this article is interesting. The paper well written and the objectives is presented very clearly. However, the authors may revise this article to enhence its quality by considering the following concerns:
1. Reference [1] was missed 
2. Please revise the format of the mentioning references in the text
3. If figures were borrowed from other literature, it is necessary to provide a link to them.
4. List of references has some style problem. The style of some references is inconsistence with the format required.
5. The recent references should be added in the paper. Literature should be from the last 5-7 years 
6. Strong motivation and arguments are required to include to justify the advantages and disadvantages of the proposed method.
7. Introduction part should contain problem statement and the novelety of the research.
Title: Diabetes Risk Prediction With Different Machine Learning Algorithms
Abstract: Diabetes is a chronic disease that causes deaths all around the world, and become worse if not detected at early stage. Therefore, it is essential to detect the disease at early stages before it starts to damage the human body. At this point, detecting diabetes becomes a significant process that need to be performed efficiently. To predict diabetes risk, machine learning algorithms can be used to make prediction when a proper dataset is given. This research introduces some potentially useful machine learning algorithms to predict diabetes risk, guides the practical usage of these algorithms and discusses the comparative results. On this manner, three different machine learning algorithms and one feature selection method are implemented to an open-source dataset. The implementation results presented the applicability of the algorithms to predict diabetes risk.
Keywords: Diabetes Disease;Machine Learning;SVM;Decision Tree;Random Forest