Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Research and development of an information system for speech recognition based on neural network algorithms metrics
Abstract: This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields.
Keywords: Artificial Intelligence;speech recognition;accuracy;machine learning;recognition model
Review score: Moderate
Review comment:   This research presents a detailed comparison, between two speech recognition models. highlighting the effective use of the Levenshtein method for evaluation recognition accuracy and speed. The employment of visualization of results enchances the comprehensibility of the study's findings. 
For improvement, it is suggested to include last paragraph to the introduction section summarizing the clear indication of the paper's novel contributions, importance and potential impacts, for better readers engagement and understanding from the outset. 
Please check correctness of affilations: The International University of Information Technology, college at? Almaty University of Power Engineering and Telecommunications named after G.Daukeyev.
Title: Research and development of an information system for speech recognition based on neural network algorithms metrics
Abstract: This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields.
Keywords: Artificial Intelligence;speech recognition;accuracy;machine learning;recognition model
Review score: High
Review comment: The abstract and keywords need technical corrections.
Pay attention to:
- formulas and provide links to sources of information;
- design of the figures.
The conclusions should summarize the results of the research and the scientific novelty seen by the authors. I also recommend emphasizing how your system differs from others.
Title: Classification of Brain Strokes in Computed Tomography Images Utilizing Deep Learning
Abstract: The growing importance of efficient and accurate medical image classification has led to increased research interest in the application of deep learning techniques. This research paper investigates the utilization of Convolutional Neural Networks (CNNs) for the classification of brain strokes from computed tomography (CT) images. Leveraging a diverse dataset, we systematically examine the performance of the CNN model in distinguishing between 'Normal' and 'Stroke' image classes. The study employs a comprehensive set of evaluation metrics, including accuracy, precision, recall, and F-score, to assess the model's diagnostic efficacy. The training process is rigorously monitored, ensuring convergence of learning rates and model stability. Our results demonstrate promising levels of accuracy and an increasing trend in precision, recall, and F-score, underscoring the potential of deep learning in enhancing stroke diagnosis. Furthermore, the study addresses data scarcity challenges through data augmentation techniques, emphasizing their pivotal role in model training. While acknowledging the need for larger and more diverse datasets and potential model enhancements, this research contributes to the broader goal of harnessing artificial intelligence for improved healthcare, particularly in the timely and accurate detection of critical medical conditions like brain strokes through non-invasive imaging modalities.
Keywords: deep learning;CNN;stroke classification;computed tomography;image analysis;image processing
Review score: High
Review comment: The topic is relevant.  The use of CNN, used to classify brain strokes, is described in clear, understandable language. 
What are the disadvantages of CST in detecting brain lesions?
Title: Classification of Brain Strokes in Computed Tomography Images Utilizing Deep Learning
Abstract: The growing importance of efficient and accurate medical image classification has led to increased research interest in the application of deep learning techniques. This research paper investigates the utilization of Convolutional Neural Networks (CNNs) for the classification of brain strokes from computed tomography (CT) images. Leveraging a diverse dataset, we systematically examine the performance of the CNN model in distinguishing between 'Normal' and 'Stroke' image classes. The study employs a comprehensive set of evaluation metrics, including accuracy, precision, recall, and F-score, to assess the model's diagnostic efficacy. The training process is rigorously monitored, ensuring convergence of learning rates and model stability. Our results demonstrate promising levels of accuracy and an increasing trend in precision, recall, and F-score, underscoring the potential of deep learning in enhancing stroke diagnosis. Furthermore, the study addresses data scarcity challenges through data augmentation techniques, emphasizing their pivotal role in model training. While acknowledging the need for larger and more diverse datasets and potential model enhancements, this research contributes to the broader goal of harnessing artificial intelligence for improved healthcare, particularly in the timely and accurate detection of critical medical conditions like brain strokes through non-invasive imaging modalities.
Keywords: deep learning;CNN;stroke classification;computed tomography;image analysis;image processing
Review score: Low
Review comment: The overall scientific idea in the paper is good, however the contribution of the paper itself is quite moderate. Authors have not properly showcased the contribution of their paper with respect to previous published literature.
Title: Enhanced UNet Approach for Detecting Stroke Lesions in Computed Tomography Brain Scans
Abstract: This research introduces an advanced UNet architecture for the precise segmentation of stroke lesions in Computed Tomography (CT) brain scans, addressing the critical need for accurate and efficient diagnosis in clinical settings. The literature reveals a spectrum of technologies utilized in detecting brain lesions, ranging from manual segmentation to threshold-based techniques and conventional machine learning methods, each with its limitations in terms of accuracy, time efficiency, and generalization capability. Deep learning techniques, notably the UNet model, have emerged as promising alternatives, offering significant advancements in medical image segmentation due to their ability to learn complex patterns and features from data. Our enhanced UNet model incorporates innovative techniques such as data augmentation, dropout, the Adam optimization algorithm, L2 regularization, and instance normalization to address the inherent challenges in stroke lesion segmentation, including the subtle appearance of lesions and the variability across different cases. The comparison of our approach with traditional methods using the ISLES 2018 dataset demonstrates substantial improvements in key performance metrics such as dice/f1 score, precision, recall/sensitivity, and Jaccard index, highlighting the effectiveness of our model in achieving precise segmentation outcomes. The utilization of these advanced techniques enables our model to outperform existing methods, offering a significant advantage in the rapid and accurate segmentation of stroke lesions. This research underscores the potential of the UNet architecture, augmented with modern deep learning techniques, to set a new benchmark in the field of stroke lesion detection, providing a robust tool for enhancing diagnostic processes and patient care in neurology.
Keywords: UNet;deep learning;stroke lesion segmentation;computed tomography;image processing
Review score: Moderate
Review comment: The topic of detecting brain lesions using new technologies is relevant. In the abstract and in the introduction, it is necessary to mention alternative technologies used in the detection of brain lesions.  In conclusion, it is necessary to add what is the advantage of the UNet approach over other methods.
Title: Enhanced UNet Approach for Detecting Stroke Lesions in Computed Tomography Brain Scans
Abstract: This research introduces an advanced UNet architecture for the precise segmentation of stroke lesions in Computed Tomography (CT) brain scans, addressing the critical need for accurate and efficient diagnosis in clinical settings. The literature reveals a spectrum of technologies utilized in detecting brain lesions, ranging from manual segmentation to threshold-based techniques and conventional machine learning methods, each with its limitations in terms of accuracy, time efficiency, and generalization capability. Deep learning techniques, notably the UNet model, have emerged as promising alternatives, offering significant advancements in medical image segmentation due to their ability to learn complex patterns and features from data. Our enhanced UNet model incorporates innovative techniques such as data augmentation, dropout, the Adam optimization algorithm, L2 regularization, and instance normalization to address the inherent challenges in stroke lesion segmentation, including the subtle appearance of lesions and the variability across different cases. The comparison of our approach with traditional methods using the ISLES 2018 dataset demonstrates substantial improvements in key performance metrics such as dice/f1 score, precision, recall/sensitivity, and Jaccard index, highlighting the effectiveness of our model in achieving precise segmentation outcomes. The utilization of these advanced techniques enables our model to outperform existing methods, offering a significant advantage in the rapid and accurate segmentation of stroke lesions. This research underscores the potential of the UNet architecture, augmented with modern deep learning techniques, to set a new benchmark in the field of stroke lesion detection, providing a robust tool for enhancing diagnostic processes and patient care in neurology.
Keywords: UNet;deep learning;stroke lesion segmentation;computed tomography;image processing
Review score: Moderate
Review comment: The research is presenting a new approach for the precise identification and segmentation of stroke lesions brain CT scans. The work is well-organized and reported. It is recommended to only highlight the contribution of the paper.
Title: Research and development of two-factor authentication methods using neural networks
Abstract: This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital
Keywords: Neural networks;biometric authentication;deep learning;digital security;identification
Review score: High
Review comment: This article introduces an innovative two-factor authentication system employing neural networks to bolster digital security, moving beyond traditional password-based methods. By leveraging advanced Python libraries like Keras, TensorFlow, and OpenCV for neural network and image processing, this approach combines face scanning and Time-based One-Time Passwords (TOTP) to offer a secure yet user-friendly authentication solution. Unlike conventional methods, which often compromise either security or convenience, this system demonstrates superior accuracy and performance in comparison to password hashing and one-time passwords. Developed using Python and Pycharm, and trained on datasets of face images and TOTP codes, this method represents a significant advancement in authentication technology, offering a blend of high security and ease of use.
Title: Research and development of two-factor authentication methods using neural networks
Abstract: This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital
Keywords: Neural networks;biometric authentication;deep learning;digital security;identification
Review score: Low
Review comment: -
Title: Research and development of two-factor authentication methods using neural networks
Abstract: This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital
Keywords: Neural networks;biometric authentication;deep learning;digital security;identification
Review score: Moderate
Review comment: The authors should submit a camera-ready paper of 4-6 pages.
The main question is how relevant an approach that combines facial recognition and TOTP as two-factor authentication can be.
Title: Development of river flow modeling methodology
Abstract: The article is devoted to the development of methodology of river runoff modeling. This paper reviews possible approaches to fluid flow modeling, analyzing existing solutions and formulations of the research problem. The Navier-Stoke equation is replaced by a difference analogue, which is subsequently solved by a numerical method. A general description of the problem is given, and the objectives of the study are formulated. Flood and inundation monitoring methods developed in Kazakhstan are described, the results of their practical use in some areas are discussed, and directions for further development are outlined. Possible ways of solving the problem of flooding of territories because of flooding are given. This information is then used to forecast emergencies.
Keywords: flooding;flood waves;breakout waves;computer modeling
Review score: Low
Review comment: 1. Result part is missing and Noting to support the model after methodology. 
2. . methodology needs to be more elborated/explained . It is not clealry formed.
3. Ciation of the reference is not properly done .
4. The Flow of the paper is not up to the mark and as per requirments .
5. All equation are not in the format and propoer numbered and no explnation found about the equation.
6. Conclusion is not clear and No future scope included .
Title: Development of river flow modeling methodology
Abstract: The article is devoted to the development of methodology of river runoff modeling. This paper reviews possible approaches to fluid flow modeling, analyzing existing solutions and formulations of the research problem. The Navier-Stoke equation is replaced by a difference analogue, which is subsequently solved by a numerical method. A general description of the problem is given, and the objectives of the study are formulated. Flood and inundation monitoring methods developed in Kazakhstan are described, the results of their practical use in some areas are discussed, and directions for further development are outlined. Possible ways of solving the problem of flooding of territories because of flooding are given. This information is then used to forecast emergencies.
Keywords: flooding;flood waves;breakout waves;computer modeling
Review score: Moderate
Review comment: - Abstract and other part: need to moderate;
- Introduction: The introduction does not contain a clear statement of the research aim. There is a lack of literature review on the research topic;
- It is recommended to disclose the research methodology.
Number equations consecutively with equation numbers in parentheses flush with the right margin, as in (1);
- some equations are not displayed correctly;
- Requirements for the submitted work in relation to citation are not met.
Title: Development of river flow modeling methodology
Abstract: The article is devoted to the development of methodology of river runoff modeling. This paper reviews possible approaches to fluid flow modeling, analyzing existing solutions and formulations of the research problem. The Navier-Stoke equation is replaced by a difference analogue, which is subsequently solved by a numerical method. A general description of the problem is given, and the objectives of the study are formulated. Flood and inundation monitoring methods developed in Kazakhstan are described, the results of their practical use in some areas are discussed, and directions for further development are outlined. Possible ways of solving the problem of flooding of territories because of flooding are given. This information is then used to forecast emergencies.
Keywords: flooding;flood waves;breakout waves;computer modeling
Review score: Moderate
Review comment: This paper falls within the scope of the conference and can be accepted for presentation after the minor improvements. It would be better if the author makes some changes in the article:
- Page 1. I have a doubt regarding the word "our country". It is preferable to use the name of the country.
- Pages 2-3. Check the formulas, please. Most of them are not in a readable format. For instance, see the Formula 1.1.
- Pages 1-3. In the Section Results, there is no information about about the computational time, the type of computer used neither the platform/language used for simulations.
- Page 3. In the Section Conclusion, complete the second sentence. What is the value of "Reynolds number"?
Title: Deep Learning based Framework for Option Price Forecasting
Abstract: This study investigates the effectiveness of a novel ConvLSTM network in predicting option prices within financial markets. Option pricing remains a pivotal aspect of financial decision-making, yet traditional models often struggle to capture the intricate dynamics inherent in market behavior. Leveraging the capabilities of deep learning, particularly the fusion of CNN and LSTM architectures, the proposed ConvLSTM model aims to address these limitations by effectively learning spatial and temporal dependencies within financial data. Through empirical evaluation utilizing data collected from the US stock markets spanning from April 2018 to June 2020, the ConvLSTM model demonstrates remarkable predictive accuracy, achieving a precision rate of approximately 92% over 200 epochs. Furthermore, visual representations corroborate the model's efficacy, showcasing its ability to closely align predicted option prices with observed values. This research contributes to the ongoing discourse on deep learning applications in financial forecasting, offering insights into the potential of ConvLSTM networks to enhance risk management strategies and inform investment decisions in dynamic and volatile market environments.
Keywords: Option price forecasting;artificial intelligence;deep learning
Review score: Moderate
Review comment: Dear Author,

The theme of your article aligns well with the conference's focus, and the proposition of a new neural network model is noteworthy. However, there is a significant issue regarding the evaluation of your model. The absence of comparative analysis with existing state-of-the-art approaches on established benchmark (gold standard) datasets makes it challenging to objectively assess the novelty and performance of your proposed model. To strengthen your manuscript, we recommend conducting and including a comprehensive evaluation of your model on these gold standard datasets. This addition would greatly enhance the validity and impact of your research.