Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Use of Deep Learning Methodologies in Combination with Reinforcement Techniques within Autonomous Mobile Cyber-physical Systems
Abstract: This study proposes a solution to the problem that arises during the development and operation of cyber-physical systems (CPS) based on the capabilities of artificial intelligence (AI). The study presents a comprehensive overview of CPS and their interactions with the environment. It offers a general classification of deep reinforcement learning algorithms, followed by an in-depth analysis of each algorithm. The current utilization of deep learning technologies within autonomous mobile cyber-physical systems is discussed. It proposes various approaches to integrate deep learning technologies with reinforcement learning in autonomous mobile CPS. Additionally, it provides a generalized structural diagram illustrating the incorporation of a deep learning module with reinforcement into CPS. The result of the research is the use of deep learning technology to improve the performance of autonomous mobile cyber-physical systems.
Keywords: deep reinforcement learning (DRL);cyber-physical system (CPS);deep learning;artificial intelligence (AI)
Review score: Moderate
Review comment: The paper's topic is very relevant but the authors should improve the structure of the manuscript. There is no section on related works and experimental part. It would be very useful to include some experimental results and data for assessing of validity of conclusions.
Title: Predicting Oil Recovery Coefficient With Machine Learning Methods on Data Generated From the Buckley-Leverett Model
Abstract: The pursuit of enhancing oil extraction processes is now embracing technological advancements, particularly in machine learning (ML), to address the inefficiencies and environmental challenges inherent in conventional methods. This research explores the use of ML algorithms for accurately predicting the Oil Recovery Coefficient (COR), an essential metric for evaluating the effectiveness of oil extraction strategies. Leveraging synthetic data generated through the Buckley-Leverett model, which incorporates variables such as viscosity, porosity, time, and production rate, this study rigorously assesses five ML models: Linear Regressors, Random Forest Regressors, Extreme Gradient Boosting Regressors, Support Vector Machines, and Artificial Neural Networks. Our findings demonstrate that Artificial Neural Networks and Random Forest Regressors notably excel, with R-squared values reaching 0.99981 and 0.99821, respectively, on test datasets. These achievements highlight the superior predictive precision and dependability of these models in estimating COR, marking a significant advancement over existing prediction techniques. In conclusion, this investigation not only evidences the transformative potential of machine learning in refining oil recovery forecasts.
Keywords: machine learning;EOR;Buckley-Leverett;oil recovery coefficient;predictive analytics;artificial neural network
Review score: Moderate
Review comment: While noting with appreciation the work, I think that some improvements are needed. For abstract, it is recommended to make amendments. In introductory part, it is recommended to give problem description in more detail. According to references, this work is part of the general research in the direction under study. Will it be possible, if authors emphasize the contribution into this research direction.
Title: Artificial Intelligent Applications in Smart Cities
Abstract: History shows us that people move towards a more stable lifestyle after undergoing significant changes. During these times, the way communities and buildings were designed started to change too. Different periods in history are known for their standout moments, each bringing its own set of needs and tech breakthroughs. Nowadays, tech keeps evolving. People nowadays aim for a more straightforward life, and technology has been shaping up to meet this need. We live in times highlighted by the rise of smart technology, like artificial intelligence and the Internet of Things, which not only make daily tasks easier but also transform how we think about our cities and personal spaces. This piece focuses on exploring the smart city idea, touching upon various associated subjects. As we delve into these areas, we'll also look at some important research related to them.
Keywords: Artificial intelligence;Smart City;Internet of Things;intelligence systems
Review score: High
Review comment: Please follow the requirement: "The authors should submit a camera-ready paper of 4-6 pages."
Please include in the abstract the main findings of the study.
Title: Artificial Intelligent Applications in Smart Cities
Abstract: History shows us that people move towards a more stable lifestyle after undergoing significant changes. During these times, the way communities and buildings were designed started to change too. Different periods in history are known for their standout moments, each bringing its own set of needs and tech breakthroughs. Nowadays, tech keeps evolving. People nowadays aim for a more straightforward life, and technology has been shaping up to meet this need. We live in times highlighted by the rise of smart technology, like artificial intelligence and the Internet of Things, which not only make daily tasks easier but also transform how we think about our cities and personal spaces. This piece focuses on exploring the smart city idea, touching upon various associated subjects. As we delve into these areas, we'll also look at some important research related to them.
Keywords: Artificial intelligence;Smart City;Internet of Things;intelligence systems
Review score: Moderate
Review comment: The article is a review, which is not suitable for the conference. The article requires a radical revision
Title: Tools For Assessing The Competitiveness Of A Construction Company As A Contractor In Public-Private Partnership Projects
Abstract: Public-private partnership enables the state and the private sector to obtain mutual benefit by combining financial, management, and technological resources, combining the solution of socially significant tasks of the country's socioeconomic development with the achievement of the interests of business and the community. The article is devoted to the study of tools for assessing the competitiveness of a construction enterprise as an executor in public-private partnership projects. The main research methods were: comparison method; methods of analysis and synthesis; method of abstraction and method of formalization. In the course of the study, the theoretical approaches to the definition of public-private partnership were summarized and the existing cases of public-private partnership application in the field of work of the construction enterprise were investigated. Based on the analysis of cases, it was established that the use of public-private partnership allows to eliminate existing gaps in the work of a construction enterprise and increase the efficiency of its operation. It was determined that the positive effect of the use of public-private partnership in the field of work of a construction enterprise is manifested not only in increasing its competitiveness, but also in improving the functioning of adjacent sectors and strengthening the competitive positions of the national economy as a whole. A number of recommendations are provided for further study of the toolkit for assessing the competitiveness of a construction enterprise as an executor in public-private partnership projects within the framework of subsequent scientific works. Unfortunately, the developments currently available in domestic economic science and practice regarding the implementation of mechanisms of public-private partnership and commercial concession do not reflect the branch specifics of contract construction and the complexity of implementing the life cycle of a construction investment project on the basis of public-private partnership.
Keywords: Public-private partnership;competitiveness;construction enterprise;evaluation methods
Review score: Moderate
Review comment: 1. please follow the template 
2. the abstract is too big 
3. the article consists too many pages - recommended to shorten excluding all known materials 
4. more technical details needed to understand the used methods and algorithms 
5. more in-depth analysis required to compare the results with those from the literature
Title: Enhancing OCR Model Training with Synthetic Data: A Case Study with SROIE Dataset
Abstract: This paper investigates the impact of synthetic data on the training effectiveness of optical character recognition (OCR) models. With the rapid advancement in machine learning, the availability of diverse and extensive datasets has become crucial for developing robust OCR systems. However, the scarcity of annotated real-world data poses significant challenges. This study focuses on performing an OCR task using the SROIE dataset, which presents a real-world challenge due to prevalent issues such as paper and ink corruption, inconsistent lighting conditions, variable fonts, colors, background noise, and imaging distortions. We introduce an approach to generating synthetic data, aiming to mimic those peculiarities. By conducting thorough experiments using the SROIE dataset and a custom synthetic training dataset, we demonstrate substantial improvements in model performance, thereby underscoring the potential of synthetic data in OCR applications.
Keywords: OCR;synthetic data;SROIE dataset;poor quality text images
Review score: High
Review comment: The paper addresses a critical bottleneck in optical character recognition (OCR) development: the dearth of annotated real-world data necessary for training effective models. It hones in on the challenges presented by the SROIE dataset—a collection characterized by real-world complications like paper corruption, inconsistent lighting, and diverse fonts and backgrounds—which are emblematic of the hurdles faced in OCR tasks. The novel approach of the study lies in its generation of synthetic data designed to replicate these complexities, a strategy not widely explored in depth in existing OCR research.

By juxtaposing the performance of OCR models trained on both the SROIE dataset and a bespoke synthetic dataset, the paper reveals notable enhancements in model accuracy and reliability. This finding is significant as it validates the hypothesis that synthetic data, when carefully crafted to reflect real-world imperfections, can serve as a powerful tool in augmenting OCR training processes. It also suggests a pathway for overcoming the limitations posed by the scarcity of diversified, annotated datasets in OCR and potentially other areas of machine learning.

The utilization of synthetic data in training OCR models, as demonstrated in this paper, represents a promising avenue for research and development in the field. This approach not only mitigates the issue of data scarcity but also offers a scalable method for enhancing model performance. Furthermore, the study's methodology and findings could inspire further investigation into the creation and application of synthetic data across various domains, potentially revolutionizing how machine learning models are trained.
Title: Enhancing OCR Model Training with Synthetic Data: A Case Study with SROIE Dataset
Abstract: This paper investigates the impact of synthetic data on the training effectiveness of optical character recognition (OCR) models. With the rapid advancement in machine learning, the availability of diverse and extensive datasets has become crucial for developing robust OCR systems. However, the scarcity of annotated real-world data poses significant challenges. This study focuses on performing an OCR task using the SROIE dataset, which presents a real-world challenge due to prevalent issues such as paper and ink corruption, inconsistent lighting conditions, variable fonts, colors, background noise, and imaging distortions. We introduce an approach to generating synthetic data, aiming to mimic those peculiarities. By conducting thorough experiments using the SROIE dataset and a custom synthetic training dataset, we demonstrate substantial improvements in model performance, thereby underscoring the potential of synthetic data in OCR applications.
Keywords: OCR;synthetic data;SROIE dataset;poor quality text images
Review score: Low
Review comment: Low contribution to research area. OCR has state-of-art solutions.  Data syntheses process is straightforward, archived cropping SROIE images. (1-3) formulas for rotations and etc are well-knonw, B.C. stages needs justification (or references).
Title: HRBwSD: Hybrid radial based with standard deviation
Abstract: The problem of class imbalance has become a relevant topic in the field of machine learning, and there are several ways to solve this problem, including oversampling, undersampling, and hybrid algorithms. The HRBwSD algorithm proposed in the research belongs to the category of hybrid algorithms. The algorithm was created based on the RBU (Radial-Based Undersampling) and RBO (Radial-Based Oversampling) algorithms, which take into account the exclusion of duplicates and the preservation of the original class distribution. The results show that the model estimates obtained using the proposed algorithm were comparable to the estimates obtained using other class balancing algorithms. This confirms the importance of adapting algorithms to the specifics of the data to obtain more accurate and fair results.
Keywords: imbalanced data;radial basis;hybrid;undersampling;oversampling
Review score: Moderate
Review comment: Dear authors, I've noticed that the conclusion section closely echoes the content of the previous section. Therefore, I recommend that you refine your conclusions to provide more specific and distinct insights.
Title: HRBwSD: Hybrid radial based with standard deviation
Abstract: The problem of class imbalance has become a relevant topic in the field of machine learning, and there are several ways to solve this problem, including oversampling, undersampling, and hybrid algorithms. The HRBwSD algorithm proposed in the research belongs to the category of hybrid algorithms. The algorithm was created based on the RBU (Radial-Based Undersampling) and RBO (Radial-Based Oversampling) algorithms, which take into account the exclusion of duplicates and the preservation of the original class distribution. The results show that the model estimates obtained using the proposed algorithm were comparable to the estimates obtained using other class balancing algorithms. This confirms the importance of adapting algorithms to the specifics of the data to obtain more accurate and fair results.
Keywords: imbalanced data;radial basis;hybrid;undersampling;oversampling
Review score: High
Review comment: -
Title: A Machine Learning Approach for Predicting Temperature and Precipitation Patterns
Abstract: Climate modeling is one of the landmark important topics. This research explores the integration of machine learning techniques into climate modeling, aiming to develop a simplified model for predicting temperature and precipitation based on location and time. It begins with an analysis of existing climate classification systems and the potential for machine learning to enhance predictive capabilities. In this paper, climate data were obtained and processed, and different features were used for experimentations to deploy artificial neural networks. After various settings with different features were experimented, the final model exhibited improved accuracy with mean absolute error (MAE) decline was given. According to results, one hidden layer with two neuron network yields 2.04 and 30.12 errors, and one hidden layer with three neuron network yields 2.23 and 30.51 errors in terms of MAE metric.
Keywords: Climate modeling;Köppen climate classification;Machine learning;Artificial Neural Networks;Precipitation forecasting;Temperature prediction
Review score: Moderate
Review comment: The research is important for climate modeling. However, several comments need to be corrected:
1.	An abstract should be made so that other authors would want to read the article. The quantitative results obtained should also be indicated in the abstract. 
2.	The literature analysis should be expanded. The literature review should be critical. The novelty of your research should be apparent from the literature review. 
3.	Sections of the article should be named more specifically.
4.	The article should be formatted strictly by the IEEE requirements, and the list of references should be corrected.
5.	The discussion should include answers to the following questions:
- How can the results be explained?
- What research will be carried out in the future?
Title: A Machine Learning Approach for Predicting Temperature and Precipitation Patterns
Abstract: Climate modeling is one of the landmark important topics. This research explores the integration of machine learning techniques into climate modeling, aiming to develop a simplified model for predicting temperature and precipitation based on location and time. It begins with an analysis of existing climate classification systems and the potential for machine learning to enhance predictive capabilities. In this paper, climate data were obtained and processed, and different features were used for experimentations to deploy artificial neural networks. After various settings with different features were experimented, the final model exhibited improved accuracy with mean absolute error (MAE) decline was given. According to results, one hidden layer with two neuron network yields 2.04 and 30.12 errors, and one hidden layer with three neuron network yields 2.23 and 30.51 errors in terms of MAE metric.
Keywords: Climate modeling;Köppen climate classification;Machine learning;Artificial Neural Networks;Precipitation forecasting;Temperature prediction
Review score: High
Review comment: The paper explores the integration of machine learning models into climate modelling for predicting the monthly temperature and precipitation based on location and time. The paper effectively demonstrates how machine learning can be applied to climate prediction, providing a comprehensive approach from data collection to model evaluation. 
Minor suggestions for improvement include increasing the resolution of Fig.1, and a proofreading to correct minor grammatical or typographical errors, (e.g. "Predicted precipitation in July in LA = 116 mm. Expected = 2mm" (Section V) according to Table 3, predicted is 118m not 116 for LA.)  
Likewise, adding a last paragraph to the Introduction section summarizing the novelties, contribution, and importance of the paper would be beneficial.
Title: Statistical analysis of the pecto-intercostal fascial plane block on pain-related outcomes in cardiac surgery
Abstract: Treatment of pain is a primary objective in medical hospitals and various plane blocks are used for this purpose. However, the effect of the pecto-intercostal fascial plane block (PIFPB) in cardiac surgery was not studied systematically. In this work, we conduct a statistical analysis of reported data from different countries. The objective of our study was to evaluate pain reduction after the PIFPB in cardiac surgery. We found two studies that reported slightly different pain scores, namely, pain measured at rest and with movement. The model favors the experimental (bupivacaine, ropivacaine) group over the control (placebo) group in terms of postoperative pain (SMD with 95% CI is –1.29 [–1.68, –0.89], p-value < 0.00001). However, the total length of hospital stay was longer in the experimental group with a mean difference of 1.35 days with 95% CI [1.04, 1.66] and p-value < 0.00001. The model showed no significant difference between groups in opioid consumption. To summarize, PIFPB after cardiac surgery reduces pain intensity scores but results in longer hospital stays. More trials should be done in the future to examine the effect of PIFPB on other medical outcomes.
Keywords: pain reduction;plane blocks;opioid consumption;meta-analysis;evidence-based medicine
Review score: High
Review comment: This paper discusses about statistical analysis PIFPB in cardiac surgery. Some parts of paper should be improved for further acceptance status:
1) If you're citing more than 1 paper then it should be written in the following format: [1-3] instead of [1],[2],[3]. Please correct these situations when it is required
2) Methodology chapter should be extended. Please provide your readers with detailed explanation 
3) Conclusion is very short.
Title: Statistical analysis of the pecto-intercostal fascial plane block on pain-related outcomes in cardiac surgery
Abstract: Treatment of pain is a primary objective in medical hospitals and various plane blocks are used for this purpose. However, the effect of the pecto-intercostal fascial plane block (PIFPB) in cardiac surgery was not studied systematically. In this work, we conduct a statistical analysis of reported data from different countries. The objective of our study was to evaluate pain reduction after the PIFPB in cardiac surgery. We found two studies that reported slightly different pain scores, namely, pain measured at rest and with movement. The model favors the experimental (bupivacaine, ropivacaine) group over the control (placebo) group in terms of postoperative pain (SMD with 95% CI is –1.29 [–1.68, –0.89], p-value < 0.00001). However, the total length of hospital stay was longer in the experimental group with a mean difference of 1.35 days with 95% CI [1.04, 1.66] and p-value < 0.00001. The model showed no significant difference between groups in opioid consumption. To summarize, PIFPB after cardiac surgery reduces pain intensity scores but results in longer hospital stays. More trials should be done in the future to examine the effect of PIFPB on other medical outcomes.
Keywords: pain reduction;plane blocks;opioid consumption;meta-analysis;evidence-based medicine
Review score: High
Review comment: The conclusion is about 1.5 times shorter than the abstract, disrupting the balance of the article. Figure 1 is overly extensive and contains numerous empty spaces. Strictly speaking, the article doesn't introduce novel aspects in IT technology, though it holds practical medical value. Hence, it's advisable to either enhance the mathematical framework or expand the sample size.