Arailym-tleubayeva/sist-english-corpus huggingface dataset:
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: After thoroughly reviewing the manuscript titled "Review of Machine Learning Models in Cyberbullying Detection Problem", I am pleased to provide my recommendation for its acceptance for publication.
Title: TVET IT technologies support for the water resources, agro forest shelterbelts sustainability
Abstract: Central Asia (CA) region is one of the regions of the world most affected by climate change and water shortages. The impacts include changes in precipitation patterns, more frequent temperature extremes and increased aridity, which will have a negative impact on agricultural production, threatening food, and environmental security. Awareness campaigns, lifelong blended learning, using all facilities, including technical and vocational education and training (TVET) Information technologies (IT) support are important to expand to upgrade, change the cultural habits and attitudes of water users. Complexities on the transboundary water sharing issues, overexploitation of water resources, poor flood-drought mitigation, disaster events, including earthquakes, require efficient cooperation in the proper TVET IT applications. Proper user-friendly lifelong blended learning for scientific information dissemination related to water issues will provide stronger support to increase awareness among water users and decision policy makers. TVET IT opportunities were elaborated. Kyrgyz-Kazakh water resources sustainability were analyzed as what will be reasonable to improve Dual TVET IT programs in cooperation Canadian-US colleges with Kyrgyz-Kazakh partners are targeted to develop. User-friendly TVET IT programs will be accessible for the rural regions, farmer’s needs. These efforts are novel in the CA region and will raise awareness among water users and decision makers.
Keywords: TVET;IT;water;hydrogen;Kyrgyzstan;Kazakhstan;globalization;sustainability
Review score: Moderate
Review comment: The article is devoted to solving the current problem of forecasting water resources in Kyrgyzstan and Kazakhstan. The article analyzes the international experience of using IT technologies, including geoinformation databases for modeling and forecasting the state of water resources. The article is relevant and important. There are several comments and recommendations.
The abstract identified that blended lifelong learning to disseminate scientific information related to water issues would provide stronger support for raising awareness among water users and decision makers. However, the article lacks information on how to implement such training in Kyrgyzstan and Kazakhstan. Authors can edit the abstract. Or add material to raise awareness among water users and decision makers.
Title: TVET IT technologies support for the water resources, agro forest shelterbelts sustainability
Abstract: Central Asia (CA) region is one of the regions of the world most affected by climate change and water shortages. The impacts include changes in precipitation patterns, more frequent temperature extremes and increased aridity, which will have a negative impact on agricultural production, threatening food, and environmental security. Awareness campaigns, lifelong blended learning, using all facilities, including technical and vocational education and training (TVET) Information technologies (IT) support are important to expand to upgrade, change the cultural habits and attitudes of water users. Complexities on the transboundary water sharing issues, overexploitation of water resources, poor flood-drought mitigation, disaster events, including earthquakes, require efficient cooperation in the proper TVET IT applications. Proper user-friendly lifelong blended learning for scientific information dissemination related to water issues will provide stronger support to increase awareness among water users and decision policy makers. TVET IT opportunities were elaborated. Kyrgyz-Kazakh water resources sustainability were analyzed as what will be reasonable to improve Dual TVET IT programs in cooperation Canadian-US colleges with Kyrgyz-Kazakh partners are targeted to develop. User-friendly TVET IT programs will be accessible for the rural regions, farmer’s needs. These efforts are novel in the CA region and will raise awareness among water users and decision makers.
Keywords: TVET;IT;water;hydrogen;Kyrgyzstan;Kazakhstan;globalization;sustainability
Review score: Moderate
Review comment: This article presents a predictive analysis of the necessary IT technologies to improve the sustainability of Kyrgyz-Kazakh water resources. A detailed literature review was conducted and the world's best technologies and research methods were presented.
It would be ideal to add clearly stated goals and scientific novelty to this article.
Title: Optimal Gradient Descent Algorithm for LSTM Neural Network Learning
Abstract: This research paper delves into an in-depth comparison of different gradient descent methods tailored for optimising long short-term memory (LSTM) recurrent neural networks (RNNs) using data generated from sinusoids with varying noise levels. Gradient descent is an optimisation technique extensively employed in machine learning, deep learning, and diverse scientific domains and serves as the focal point of investigation. This study's primary objective is to thoroughly comprehend the distinctive attributes, merits, and limitations of various gradient descent methods, thereby elucidating their effects on convergence speed, stability, and computational efficiency within the context of LSTM RNNs. Through meticulous analysis and experimentation, this research contributes insights essential for informed decision-making in selecting and deploying gradient descent algorithms for LSTM RNN optimisation, thereby advancing the efficacy and reliability of recurrent neural network applications in real-world scenarios.
Keywords: Reccurent Neural Network;Learning Algorithm;Stochastic gradient descent;Nadam;Adadelta;RMSprop;Adam;Adagrad;Mean Squared Error;Arithmetic Mean
Title: Human-Computer Testing of Students’ Knowledge
Abstract: The modern rapid development of means and technologies for education is aimed at reducing classroom classes and remote control of knowledge and skills of education seekers. The adaptation of the education system to the remote regime has become especially relevant throughout the world during the period of quarantine restrictions caused by COVID-19. In Ukraine, the urgency of developing and implementing info-communication systems in the education process has been intensified by the martial law since February 24, 2022. The urgent need to rebuild a large number of buildings and structures destroyed and damaged by hostilities makes special demands on graduates of construction educational institutions. However, the fundamental studying of specialists in this field in remote mode requires specialized info-communication systems, creating of which need taking part of specialists in the field. On-line testing is the main means of guaranteeing the quality of the modern educational process. Therefore, the article presents the specialized on-line testing system "Fast Knowledge Test", which was developed at the Kyiv National University of Construction and Architecture with the participation of stakeholders. The system operates during 2021-2023 and while the monitoring of students’ knowledge and skills remains with the teacher, but the service provides for its automation.
Keywords: fast on-line testing system;physics;website;working with tests
Review score: Moderate
Review comment: The article describes a specialized online knowledge testing system called "Fast Knowledge Test," developed at the Kyiv National University of Construction and Architecture. This system automates monitoring students' knowledge and skills and is based on various technologies. The work's primary focus is exploring the capabilities of a specialist administrator managing task content. The article also emphasizes the evaluation of the experiment conducted with the participation of students from different specialties and the results obtained.
However, there are some notable points for improvement and considerations:
1. It is necessary to clearly formulate the scientific novelty of the research, its tasks and goals. conclusions should be correlated with tasks.
2. It is crucial to acknowledge any restrictions or difficulties encountered during the implementation of the testing system. Providing an overview of the challenges faced and how they were resolved, or identifying potential areas for enhancement, would offer a more comprehensive understanding of the system's evolution.
Title: Human-Computer Testing of Students’ Knowledge
Abstract: The modern rapid development of means and technologies for education is aimed at reducing classroom classes and remote control of knowledge and skills of education seekers. The adaptation of the education system to the remote regime has become especially relevant throughout the world during the period of quarantine restrictions caused by COVID-19. In Ukraine, the urgency of developing and implementing info-communication systems in the education process has been intensified by the martial law since February 24, 2022. The urgent need to rebuild a large number of buildings and structures destroyed and damaged by hostilities makes special demands on graduates of construction educational institutions. However, the fundamental studying of specialists in this field in remote mode requires specialized info-communication systems, creating of which need taking part of specialists in the field. On-line testing is the main means of guaranteeing the quality of the modern educational process. Therefore, the article presents the specialized on-line testing system "Fast Knowledge Test", which was developed at the Kyiv National University of Construction and Architecture with the participation of stakeholders. The system operates during 2021-2023 and while the monitoring of students’ knowledge and skills remains with the teacher, but the service provides for its automation.
Keywords: fast on-line testing system;physics;website;working with tests
Review score: Moderate
Review comment: Although the topic is generally relevant for research.
You need to make corrections to the article. It is necessary to describe the research results in more detail and indicate the limitations of the study.
Title: Development of a Digital Stethoscopy System for Detection of Heart Diseases
Abstract: This research paper presents the development of a Digital Stethoscopy System for the Detection of Heart Diseases using machine learning techniques on phonocardiograms. With cardiovascular diseases remaining a leading cause of global morbidity and mortality, the need for accurate and timely cardiac diagnostics is paramount. The proposed system integrates cutting-edge technology, including digital stethoscopy and machine learning, to address the limitations of traditional cardiac auscultation methods. The core components of the system, including phonocardiographic signal acquisition, an advanced Analog Front End, a robust microcontroller, and sophisticated machine learning algorithms, are meticulously designed and evaluated. Extensive data preparation, model training, and rigorous testing are undertaken to assess the system's diagnostic capabilities. Results indicate that this innovative system has the potential to provide reliable and real-time cardiac diagnostics, offering a promising solution for early detection and intervention in heart diseases. Furthermore, the research underscores the importance of large and diverse datasets for training machine learning models and emphasizes the significance of continuous validation and evaluation. The outcomes of this study hold promise for improving cardiac healthcare, enhancing patient outcomes, and advancing the field of cardiology, with recommendations for further research and clinical validation to refine and optimize the system for widespread clinical implementation.
Keywords: machine learning;phonocardiogram;heart sounds;stethoscope;sound analysis
Review score: Moderate
Review comment: The article "Development of a Digital Stethoscopy System for Detection of Heart Diseases" is relevant and has valuable practical significance.
Disadvantages of work:
- it is necessary to mathematically describe the operation of the stethoscope presented in the article;
- add information about the machine learning model that will be used in "Digital Stethoscopy System", a software environment for machine learning;
- describe the software offered in "Digital Stethoscopy System";
- describe the scientific novelty of the article;
- add quantitative data, for example, model accuracy.
The work can be accepted after revision.
Title: Development of a Digital Stethoscopy System for Detection of Heart Diseases
Abstract: This research paper presents the development of a Digital Stethoscopy System for the Detection of Heart Diseases using machine learning techniques on phonocardiograms. With cardiovascular diseases remaining a leading cause of global morbidity and mortality, the need for accurate and timely cardiac diagnostics is paramount. The proposed system integrates cutting-edge technology, including digital stethoscopy and machine learning, to address the limitations of traditional cardiac auscultation methods. The core components of the system, including phonocardiographic signal acquisition, an advanced Analog Front End, a robust microcontroller, and sophisticated machine learning algorithms, are meticulously designed and evaluated. Extensive data preparation, model training, and rigorous testing are undertaken to assess the system's diagnostic capabilities. Results indicate that this innovative system has the potential to provide reliable and real-time cardiac diagnostics, offering a promising solution for early detection and intervention in heart diseases. Furthermore, the research underscores the importance of large and diverse datasets for training machine learning models and emphasizes the significance of continuous validation and evaluation. The outcomes of this study hold promise for improving cardiac healthcare, enhancing patient outcomes, and advancing the field of cardiology, with recommendations for further research and clinical validation to refine and optimize the system for widespread clinical implementation.
Keywords: machine learning;phonocardiogram;heart sounds;stethoscope;sound analysis
Review score: Moderate
Review comment: The article is dedicated to the current topic of the application of machine learning in medical diagnostics. The authors have developed the concept of a digital stethoscope, as well as the device itself. The article is written in understandable scientific language. The following clarifications are recommended:

1. On what data was the model trained and tested?
2. In the Experimental Results section, four acoustic patterns are shown. Is this a complete list of possible patterns or only a part? Can the model identify all possible patterns?
3. In the abstract, you state that "Results indicate that this innovative system has the potential to provide reliable and real-time cardiac diagnostics…". What results are being referred to? Judging by the conclusion, clinical trials have not yet been conducted. How was the effectiveness of the model evaluated, and what results were achieved?
Title: HSTL IO Standards Based Low Power Implementation of Elliptic Curve Cryptography (ECC) on FPGA
Abstract: There is a new trend in researcher to use elliptical curve theory for derivation of an efficient, faster and smaller key that will use in lightweight cryptography also known as elliptical curve cryptography (ECC). In this work, we are using High Speed Transceiver Logic (HSTL) IO standard to develop an energy efficient ECC and implementing the Hardware Description Language (HDL) code of ECC on Kintex-7 Field Programmable Gate Array (FPGA). HSTL_II_18 is taking 53.73 % more power in comparison to HSTL_I IO standards. The implementation with HSTL_I or HSTL_II_18 will deliver same result of encryption but power requirement will change. Wise selection of IO standards playing an important role in overall power dissipation during implementation of ECC on FPGA.
Keywords: ECC;Elliptical Curve Cryptography;HSTL;High Speed Transceiver Logic;FPGA;HDL
Review score: Moderate
Review comment: Lack of comparative analysis. The paper focuses on energy analysis of implementing ECC with different HSTL I/O standards on FPGAs. However, it lacks a comprehensive comparative analysis with existing ECC implementation methods or technologies. It is necessary to compare it with other approaches and make the research more in-depth.
Limited discussion of performance metrics. While the article discusses power consumption in detail, it does not discuss other important performance metrics such as speed, area utilization, and the overall efficiency of the ECC implementation. It is necessary to analyze a wider range of performance indicators to obtain a more holistic assessment of the proposed approach.
Inadequate discussion of safety issues. Since ECC is a cryptographic algorithm, security considerations are of paramount importance. The article does not delve into the security aspects of implementing ECC on an FPGA using HSTL IO standards. A reworking of the discussion of the security implications and vulnerabilities of the proposed implementation would have strengthened the analysis.
Lack of real-world implementation verification: The article focuses on theoretical analysis and simulation results of ECC implementation on FPGA using HSTL IO standards.
Limited exploration of future possibilities. The article does not discuss potential problems, opportunities, or research directions in detail. A more comprehensive discussion of future scope would improve the relevance and impact of the study.
Title: HSTL IO Standards Based Low Power Implementation of Elliptic Curve Cryptography (ECC) on FPGA
Abstract: There is a new trend in researcher to use elliptical curve theory for derivation of an efficient, faster and smaller key that will use in lightweight cryptography also known as elliptical curve cryptography (ECC). In this work, we are using High Speed Transceiver Logic (HSTL) IO standard to develop an energy efficient ECC and implementing the Hardware Description Language (HDL) code of ECC on Kintex-7 Field Programmable Gate Array (FPGA). HSTL_II_18 is taking 53.73 % more power in comparison to HSTL_I IO standards. The implementation with HSTL_I or HSTL_II_18 will deliver same result of encryption but power requirement will change. Wise selection of IO standards playing an important role in overall power dissipation during implementation of ECC on FPGA.
Keywords: ECC;Elliptical Curve Cryptography;HSTL;High Speed Transceiver Logic;FPGA;HDL
Review score: High
Review comment: It is necessary to clearly formulate the scientific novelty of the research, its tasks and goals, but the work is good and has a clear engineering character and has practical value. The results of practical research are given, but for which mathematical models are not specified, I recommend supplementing the article with a mathematical model of the specified algorithm, as well as a detailed description of the experiment.
Title: A Machine Learning Approach for Detection of Heart Diseases Using Wearable Devices
Abstract: This research paper delves into the integration of wearable devices and deep learning techniques to enhance heart disease detection and monitoring. The study highlights the importance of continuous cardiac health assessment and the transformative potential of wearable technology in the healthcare landscape. Our investigation showcases their efficacy in processing intricate physiological data for early heart disease recognition. Key variables such as age, maximum heart rate, blood pressure, cholesterol levels, and various electrocardiography (ECG) metrics are explored as pivotal factors in predicting and comprehending heart disease risk factors. Furthermore, we emphasize the significance of data normalization methodologies and secure data storage solutions to ensure data reliability and accessibility. Correlation analyses elucidate the intricate interplay among these variables, providing valuable insights into their collective influence on cardiac health. As the realm of machine learning and healthcare technology continues to evolve, this research lays a solid foundation for advancing heart disease detection and management, ultimately aiming to enhance patient outcomes and elevate the quality of healthcare delivery.
Keywords: artificial intelligence;machine learning;classification;detection;heart disease;wereable devices
Review score: Moderate
Review comment: The topic of the article is relevant and promising for the medical field.
"... Deep Learning Model: To process and interpret the collected data, we propose the utilization of a deep learning model. In particular, we will employ a convolutional neural network (CNN) [14] for ECG signal analysis and a recurrent neural network (RNN) [15] for temporal modeling of HR and BP data..." - it is necessary to show how such a choice is justified: literature analysis, own research, etc.
Emphasis should be placed on scientific novelty. Pay attention to the literature analysis - existing data science tools for solving the article's task.
Fig. 1 is difficult to read.
Provide quantitative indicators to the abstract and text of the article.
The work has variable designations (for example, (X_ecg(t))), but according to the text, the designations are not displayed either in the simulation or in other usage.
"...Figure 3. Compare analyses..." - expand the title.
The article needs to add an algorithm from data collection to predictive models. It is unclear what software was used.
The work is poorly mathematically formalized, there are typos. This needs to be worked out.