Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Simulation of motion control of a cylindrical robot manipulator
Abstract: In this paper, a dynamic model of a cylindrical robot manipulator of RPP (Revolute-Prismatic-Prismatic) type based on classical laws of mechanics including Euler-Newton, Kane and Lagrange approaches is developed and presented. MATLAB Robotics Toolbox tools are used to demonstrate and analyze the manipulator working environment. The main model parameters include generalized coordinate vector (q), generalized velocity vector (dt/dq) and generalized acceleration vector (dt2/dq), which provides a comprehensive approach to controlling the robot dynamics. The presented code is characterized by simplicity and high efficiency in the implementation of robot manipulator control. A key feature of the model is the ability to specify user-defined points, which allows real-time tracking and analysis of the manipulator trajectory, ensuring its adaptability and flexibility under different operating conditions.
Keywords: cylindrical robot manipulator;control of the dynamics of a robotic manipulator;modeling of motion trajectories
Review score: Low
Review comment: Do Not Use Symbols, Special Characters, or Math in  Abstract.
The paper needs reworking the abstract;
Some equations are displayed incorrectly;
Add a literature review on the research topic.
Title: Simulation of motion control of a cylindrical robot manipulator
Abstract: In this paper, a dynamic model of a cylindrical robot manipulator of RPP (Revolute-Prismatic-Prismatic) type based on classical laws of mechanics including Euler-Newton, Kane and Lagrange approaches is developed and presented. MATLAB Robotics Toolbox tools are used to demonstrate and analyze the manipulator working environment. The main model parameters include generalized coordinate vector (q), generalized velocity vector (dt/dq) and generalized acceleration vector (dt2/dq), which provides a comprehensive approach to controlling the robot dynamics. The presented code is characterized by simplicity and high efficiency in the implementation of robot manipulator control. A key feature of the model is the ability to specify user-defined points, which allows real-time tracking and analysis of the manipulator trajectory, ensuring its adaptability and flexibility under different operating conditions.
Keywords: cylindrical robot manipulator;control of the dynamics of a robotic manipulator;modeling of motion trajectories
Review score: Moderate
Review comment: Dear authors,

I did not see an amplified novelty introduction in this work, so it should be. The second thing to consider is reviewing other papers to determine if your paper has novelty. Because from the paper, it is not clear if this work just describes how to control or if it is an instruction that might exist somewhere else already. If in this is new thing, then it should be emphasized, like a new application case or new methodology.
Title: Intelligent monitoring system based on Atmega microcontrollers in healthcare with stress reduce effect
Abstract: Ensuring the well-being of people is one of the most important responsibilities in modern society. Medical Internet of Things (IoMT) plays a key role in the medical and healthcare fields. This research aims to develop a patient monitoring system using sensors and Arduino platforms, combining hardware and software components. The system includes three main sensors to collect and transmit patient health data to a central server via a network. If an anomaly is detected, the system instantly notifies the doctor with an alarm message. To evaluate the convenience and importance of the system for patients, tests were conducted on 287 people. Patient data included various characteristics such as age, gender, place of residence, hemoglobin concentration, red blood cell count and other data obtained at the last doctor's visit, as well as daily data from Arduino sensors. The effectiveness of the system was rated as high: 85% of patients expressed satisfaction, 7% were not completely satisfied, and the rest did not participate in the survey. The adequacy and accuracy of cardiovascular disease prediction was also assessed. The system's usability and ease of use received high marks from patients, who also noted its reliability compared to other monitoring methods. The use of data in the intelligent part of the system makes it possible to predict the development of cardiovascular diseases and track the progress of the disease, although this requires more research.
Keywords: Intelligent system;Stress reduce;Atmega;Microcontrollers;Monitoring
Review score: Moderate
Review comment: The paper is interesting, it is well structured and fits the Conference topics.  However, to be accepted the paper needs some improvements, in particular:
(i) authors have to describe clearer own contribution comparing the proposed approach with existing ones   
(ii) a Chapter Conclusion should be revised adding the received numeric results and directions of a future research
(iii)Reference List could be revised cutting some local references and introducing the international ones, for example: 
-O. Duda et al., "Data Processing in IoT for Smart City Systems," 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France, 2019, pp. 96-99, doi: 10.1109/IDAACS.2019.8924262.
-Talakh, M. V., Holub, S. V., Luchshev, P. O., & Turkin, I. B. (2022). Intelligent Monitoring of Air Temperature by the DATA of Satellites and Meteorological Stations. International Journal of Computing, 21(1), 120-127. https://doi.org/10.47839/ijc.21.1.2525
Title: Intelligent monitoring system based on Atmega microcontrollers in healthcare with stress reduce effect
Abstract: Ensuring the well-being of people is one of the most important responsibilities in modern society. Medical Internet of Things (IoMT) plays a key role in the medical and healthcare fields. This research aims to develop a patient monitoring system using sensors and Arduino platforms, combining hardware and software components. The system includes three main sensors to collect and transmit patient health data to a central server via a network. If an anomaly is detected, the system instantly notifies the doctor with an alarm message. To evaluate the convenience and importance of the system for patients, tests were conducted on 287 people. Patient data included various characteristics such as age, gender, place of residence, hemoglobin concentration, red blood cell count and other data obtained at the last doctor's visit, as well as daily data from Arduino sensors. The effectiveness of the system was rated as high: 85% of patients expressed satisfaction, 7% were not completely satisfied, and the rest did not participate in the survey. The adequacy and accuracy of cardiovascular disease prediction was also assessed. The system's usability and ease of use received high marks from patients, who also noted its reliability compared to other monitoring methods. The use of data in the intelligent part of the system makes it possible to predict the development of cardiovascular diseases and track the progress of the disease, although this requires more research.
Keywords: Intelligent system;Stress reduce;Atmega;Microcontrollers;Monitoring
Review score: Moderate
Review comment: The research focuses on developing a patient monitoring system using sensors and Arduino microcontrollers to reduce stress in healthcare. The system includes three primary sensors for collecting and transmitting patient health data to a central server via a network. Testing showed high system effectiveness and patient satisfaction. Using data in the intelligent part of the system allows for the prediction of cardiovascular disease development and the monitoring of disease progress. The authors of the study present mid-level scientific results. The authors have put significant effort into their work. However, certain shortcomings in the study require attention and need to be rectified.
1. The authors need to formulate the scientific novelty of the research, as well as its goals and objectives.
2. The Conclusions section should be correlated with the goals and objectives of the work.
3. The abstract of the study states that tests on 150 patients confirmed the system's validity. However, in the Results section (Table 1), 285 patients are already considered. Which of these numbers is correct?
4. The article's text does not contain links to some sources, such as 11-16, and there are also links to missing sources, such as 34. It needs to be improved.
5. The article needs more specific details about the types and models of sensors used in the patient monitoring system. 

After making the necessary revisions, the article is now suitable for acceptance into the conference.
Title: Development of Pneumonia Detection System on X-Ray Images Using Deep Learning
Abstract: This research paper explores the development of an advanced pneumonia detection system utilizing deep learning (DL) techniques on X-ray images, aiming to enhance diagnostic accuracy and efficiency in medical settings. By integrating Convolutional Neural Networks (CNNs), the study leverages the capability of DL to automatically learn and identify intricate patterns within X-ray images that are indicative of pneumonia. The proposed system undergoes rigorous testing and validation on a diverse dataset of chest X-rays, demonstrating its potential to achieve high levels of accuracy, precision, recall, and F-score, surpassing traditional diagnostic methods. Furthermore, the paper discusses the challenges encountered in the development process, including data variability, model interpretability, and the need for extensive dataset annotation. It also highlights the significance of deploying such DL-based systems in clinical environments, where they can provide real-time support to radiologists and healthcare professionals, leading to quicker and more reliable pneumonia diagnosis, ultimately improving patient outcomes and care efficiency.
Keywords: deep learning;CNN;classification;pneumonia;X-Ray;image processing
Review score: Moderate
Review comment: After thoroughly reviewing the manuscript titled "Development of Pneumonia Detection System on X-Ray Images Using Deep Learning", I am pleased to provide my recommendation for its acceptance for publication.
Title: Development of Pneumonia Detection System on X-Ray Images Using Deep Learning
Abstract: This research paper explores the development of an advanced pneumonia detection system utilizing deep learning (DL) techniques on X-ray images, aiming to enhance diagnostic accuracy and efficiency in medical settings. By integrating Convolutional Neural Networks (CNNs), the study leverages the capability of DL to automatically learn and identify intricate patterns within X-ray images that are indicative of pneumonia. The proposed system undergoes rigorous testing and validation on a diverse dataset of chest X-rays, demonstrating its potential to achieve high levels of accuracy, precision, recall, and F-score, surpassing traditional diagnostic methods. Furthermore, the paper discusses the challenges encountered in the development process, including data variability, model interpretability, and the need for extensive dataset annotation. It also highlights the significance of deploying such DL-based systems in clinical environments, where they can provide real-time support to radiologists and healthcare professionals, leading to quicker and more reliable pneumonia diagnosis, ultimately improving patient outcomes and care efficiency.
Keywords: deep learning;CNN;classification;pneumonia;X-Ray;image processing
Review score: Moderate
Review comment: The paper is well written and provides a good experimental base. Hovewer the authors should describe the source of images for example fig. 2.
Title: Multithreshold Neural Units for Multiclass Classification
Abstract: We treat multithreshold neural units and propose the model of k-threshold multi-valued neuron. The simple online learning algorithm is described, which is designed to train multi-threshold neuron to solve multiclass classification tasks and is suitable for large scale learning. We state also conditions ensuring the algorithm convergence and finiteness. The simulation results demonstrate the performance of multithreshold classifier on benchmarks compared to some classical classifiers.
Keywords: multithreshold neuron;multi-valued neuron;multitclass classification;neural network
Review score: Moderate
Review comment: The article in general  is interesting, meets the requirements and can be published. There are a number of comments that need to be corrected:
1.	An abstract should be made in such a way that other authors would want to read the article. So the quantitative results obtained should also be indicated in the abstract. 
2. Also Figure 2 is of poor quality.
Title: Multithreshold Neural Units for Multiclass Classification
Abstract: We treat multithreshold neural units and propose the model of k-threshold multi-valued neuron. The simple online learning algorithm is described, which is designed to train multi-threshold neuron to solve multiclass classification tasks and is suitable for large scale learning. We state also conditions ensuring the algorithm convergence and finiteness. The simulation results demonstrate the performance of multithreshold classifier on benchmarks compared to some classical classifiers.
Keywords: multithreshold neuron;multi-valued neuron;multitclass classification;neural network
Review score: High
Review comment: Overall, the paper is well-written and provides valuable insights into multithreshold neural units for multiclass classification. However, there are a few areas where corrections or improvements could be made: 
1)	Consider providing more specific information about the simulation results, such as the magnitude of performance improvement compared to classical classifiers.
2)	Add more details about the simulation methodology, including the specific parameters used for each classifier and any preprocessing steps applied to the datasets.
3)	Ensure that the description of the learning algorithm is concise and easy to follow for readers unfamiliar with the topic.
4)	Provide a more detailed discussion of the simulation results, including an analysis of the factors contributing to the performance differences between MultiThreshold and classical classifiers.
Title: Assessing Urbanization Levels Using GeoData: Implications for Kazakhstan Regional Development Planning
Abstract: In this study that utilized satellite data and official statistics, an analysis of the level of urbanization in the Turkestan region was undertaken. The results of this investigation unveiled a surprising revelation regarding the region's urban landscape. Among the seven cities examined, three, to be exact, did not meet the criteria as urban centers. We challenge the assumption that the Turkestan region is highly urbanized and sheds light on a different perspective on its development.The revelation that a considerable portion of the region does not qualify as functional urban areas prompts a reevaluation of our understanding of urbanization trends in Turkestan. We highlight the need to address the complexities of urban development by using available open data.The study expands the application of the methods previously used in similar research, and the results show that the actual level of urbanization in the region is less than the official figure of 56.7%. It was found to be only 36.9%. Such a significant difference between the state-approved data and the measured level of urbanization raises questions about the accuracy of the data and the methodologies used to determine urbanization.The analysis of satellite data and official statistics in the Turkestan region has revealed discrepancies in the level of urbanization, indicating a need for a more accurate assessment methodology. These findings highlight the importance of employing reliable data and appropriate methodologies to ensure effective planning and development in urban areas.There is a chance to update urban policies in the region using an open data approach. Integrating reliable data and appropriate methodologies will lead to more informed decision-making processes and better allocation of resources, ultimately contributing to the overall well-being of the communities in the Turkestan region.The scientific novelty lies in utilizing satellite data and official statistics to analyze the level of urbanization to make better decisions.
Keywords: territorial planning;degree of urbanization;urbanization;Kazakhstan;regional development
Review score: High
Review comment: The paper presents a novel method of estimating the level of urbanization in the Shymkent region of RK by using satellite imagery. It demonstrates a good application of new information technology to the standard estimation of the urbanization level.
Title: Dual-Domain Face Anti-Spoofing with Integrated Spatial and Frequency Analysis Neural Network
Abstract: In this paper, we introduce a novel dual-domain approach for face anti-spoofing, aimed at enhancing the robustness and accuracy of spoofing detection mechanisms. Our model uniquely combines spatial domain analysis with frequency domain insights, employing a two-branch and dual loss neural network architecture that leverages augmented images and their Fourier transforms during the training stage. The EfficientNetV2s pre-trained model serves as the base structure, and an additional component, termed the Fourier head, is inserted into an intermediate layer of the backbone. This component is specifically designed to reconstruct the frequency map of the input image. It helps to capture a comprehensive set of features indicative of spoofing attacks, including textural and color anomalies as well as recurrent patterns not visible in the spatial domain. Tested on the OULU-NPU dataset, our methodology has demonstrated exceptional performance, achieving near state-of-the-art results on most protocols. Thus, our model is capable of accurately distinguishing between genuine and spoofed facial images, thereby confirming the advantage of integrating spatial and frequency domain analyses for face anti-spoofing.
Keywords: face anti spoofing;deep learning;machine learning;face liveness;image processing;computer vision
Review score: High
Review comment: Abstract
In my opinion, you need to improve the abstract, as it does not fully reflect what was done
Keywords 
Remove extra signs ";" etc 
I. The subject area of the study is adequately described in the introduction
II. I consider the correct choice of optimization strategy with double losses in order to increase the accuracy of detection due to the effective use of spatial and frequency domain functions and the minimization of these losses. Also strategy to use the capabilities of Convolutional Neural Networks alongside analysis in the frequency domain is expedient.
III. The proposed model and setups featuring that was used for experiments are described in detail and clearly. Evaluation results on OULU-NPU dataset by 4 Protocols It presented.
In my opinion, you need in the conclusions, it write down more specifically the advantages of the model, which the table demonstrate Table 1.
Title: Dual-Domain Face Anti-Spoofing with Integrated Spatial and Frequency Analysis Neural Network
Abstract: In this paper, we introduce a novel dual-domain approach for face anti-spoofing, aimed at enhancing the robustness and accuracy of spoofing detection mechanisms. Our model uniquely combines spatial domain analysis with frequency domain insights, employing a two-branch and dual loss neural network architecture that leverages augmented images and their Fourier transforms during the training stage. The EfficientNetV2s pre-trained model serves as the base structure, and an additional component, termed the Fourier head, is inserted into an intermediate layer of the backbone. This component is specifically designed to reconstruct the frequency map of the input image. It helps to capture a comprehensive set of features indicative of spoofing attacks, including textural and color anomalies as well as recurrent patterns not visible in the spatial domain. Tested on the OULU-NPU dataset, our methodology has demonstrated exceptional performance, achieving near state-of-the-art results on most protocols. Thus, our model is capable of accurately distinguishing between genuine and spoofed facial images, thereby confirming the advantage of integrating spatial and frequency domain analyses for face anti-spoofing.
Keywords: face anti spoofing;deep learning;machine learning;face liveness;image processing;computer vision
Review score: High
Review comment: Positively noting the paper, and approaches applied, I would recommend to revise and make changes for abstract and sections, in order to clarify the problem statment, research methods, and conclusion. Also, it would be better if the literature is added, reffered to the use of frequency features, alongside with convolutional networks approaches.  
In addition, it make sence if name of the paper is changed, to better characterise the approaches applied and  results obtained.
Title: Review of Machine Learning Models in Cyberbullying Detection Problem
Abstract: This research paper delves into the intricate domain of cyberbullying detection on social media, addressing the pressing issue of online harassment and its implications. The study encompasses a comprehensive exploration of key aspects, including data collection and preprocessing, feature engineering, machine learning model selection and training, and the application of robust evaluation metrics. The paper underscores the pivotal role of feature engineering in enhancing model performance and highlights the versatility of supervised machine learning techniques such as Support Vector Machines, Naïve Bayes, Decision Trees, and others in the context of cyberbullying detection. Furthermore, it elucidates the significance of evaluation metrics like accuracy, precision, recall, F1-score, and AUC-ROC in quantitatively assessing model effectiveness. By providing valuable insights and methodologies, this research contributes to the ongoing efforts to combat cyberbullying, ultimately promoting safer online environments and safeguarding individuals from the pernicious effects of online harassment.
Keywords: machine learning;cyberbullying;feature engineering;feature extraction;feature selection
Review score: Moderate
Review comment: The article is devoted to a comprehensive review of machine learning models and approaches used to solve the problem of cyberbullying detection. 
The authors review a large amount of literature on machine learning-based cyberbullying detection, the evolution of methods and methodologies, from early rule-based approaches to more sophisticated deep learning algorithms.
The importance of the problem of work in today's realities corresponds to the theme of the conference and is relevant and promising. 
The paper has the following significant remarks:
1.	The work does not provide a specific solution to the problem, there is no development of models and methods.
2.	The authors have not provided new, original materials containing scientific novelty.
3. There is no connection between the explanation of the provided formulas.
4.	The formatting does not meet the requirements for a scientific paper; there are no references to figures, tables, or formulas.