Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Optimizing Microclimate Comfort in Smart Constructions Using an Intelligent Fuzzy-PID Temperature Regulation System
Abstract: This research paper investigates the implementation and performance of an smart Fuzzy-PID Temperature Control approach within the context of optimizing the microclimate in intelligent buildings. The study delves into the intricate dynamics of this innovative control system, emphasizing the critical role of precise parameter tuning and the incorporation of fuzzy logic for adaptive PID control. The research encompasses the development and application of a comprehensive fuzzy rule base, encompassing fuzzification, fuzzy inference, and defuzzification processes to facilitate dynamic parameter adaptation. Through meticulous simulations and comparative analyses with conventional control systems, the study reveals the superior performance of the proposed controller in maintaining a stable and comfortable indoor environment. Temperature stability, precision, and narrow fluctuations are hallmarks of its efficacy, contributing to enhanced comfort of residents and energy saving. The results presented in this paper highlight the promising potential of the Intelligent Fuzzy-PID Temperature Control approach in advancing the management of intelligent buildings, aligning with contemporary objectives of sustainability and resource optimization. This research underscores the significance of integrating intelligent control systems for improved indoor microclimate regulation and lays the foundation for further exploration and practical implementations in the realm of smart building management.
Keywords: comfort microclimate;comfort temperature;fuzzy logic;PID;control;intelligent system
Review score: High
Review comment: 1) Figure 1 need to be properly readable and should be at-least 300 dpi.
2) All figures titles and tables titles are not as per the IEEE format. Need to rewrite all of them again.
3) Literature review should be added in a separate section.
4) Grammatical errors need to be removed from the whole manuscript.
5) Acknowledgement section need to be added in the manuscript, as per IEEE Format requirements.
6) Abstract and conclusion sections need to be properly mapped with the whole manuscript.
Title: Optimizing Microclimate Comfort in Smart Constructions Using an Intelligent Fuzzy-PID Temperature Regulation System
Abstract: This research paper investigates the implementation and performance of an smart Fuzzy-PID Temperature Control approach within the context of optimizing the microclimate in intelligent buildings. The study delves into the intricate dynamics of this innovative control system, emphasizing the critical role of precise parameter tuning and the incorporation of fuzzy logic for adaptive PID control. The research encompasses the development and application of a comprehensive fuzzy rule base, encompassing fuzzification, fuzzy inference, and defuzzification processes to facilitate dynamic parameter adaptation. Through meticulous simulations and comparative analyses with conventional control systems, the study reveals the superior performance of the proposed controller in maintaining a stable and comfortable indoor environment. Temperature stability, precision, and narrow fluctuations are hallmarks of its efficacy, contributing to enhanced comfort of residents and energy saving. The results presented in this paper highlight the promising potential of the Intelligent Fuzzy-PID Temperature Control approach in advancing the management of intelligent buildings, aligning with contemporary objectives of sustainability and resource optimization. This research underscores the significance of integrating intelligent control systems for improved indoor microclimate regulation and lays the foundation for further exploration and practical implementations in the realm of smart building management.
Keywords: comfort microclimate;comfort temperature;fuzzy logic;PID;control;intelligent system
Review score: Moderate
Review comment: 1) Authors are strictly advised to prepare manuscript in the conference template. 
2) Advised to add literature survey part seperately .
3) Paper is technically well written .
4) All reference are not cited .
Title: Navigating Autonomously with Deep Learning-Powered Lane Line Detection for Vehicles
Abstract: The present research explores the utilization of the Mask R-CNN convolutional network in the context of real-time road lane detection and segmentation, with a particular emphasis on its applicability to diverse environmental conditions. Through systematic experimentation and analysis, our study demonstrates the efficacy and adaptability of the proposed framework. The results, as illustrated in accompanying figures, highlight the model's capability to robustly identify and segment road lanes, thus contributing to the enhancement of autonomous vehicle navigation. Importantly, our system's proficiency extends to accommodating varied weather conditions, including scenarios with sunshine, rainfall, overcast skies, and others, as well as its adaptability to both day and night conditions. The real-time decision-making capacity of the system plays a pivotal role in optimizing the maneuvering of autonomous vehicles, offering potential benefits for road safety and traffic management. Moreover, we detail our dataset collection process and thorough pre-processing steps, ensuring the accuracy of model training and evaluation. This research contributes valuable insights into the practical application of deep learning, particularly the Mask R-CNN architecture, within the realm of autonomous driving, with implications for advancing road safety, traffic optimization, and the overall efficiency of autonomous vehicle operations, opening avenues for future refinements and expansions of this approach.
Keywords: lane detection;image processing;computer vision;machine learning;deep learning
Review score: Moderate
Review comment: The research is of scientific interest. The article requires some changes in accordance with the requirements specified in the template.  Please pay attention to the design and quality of the drawings, the intervals between the drawings and the text, bold font, references.
Title: Navigating Autonomously with Deep Learning-Powered Lane Line Detection for Vehicles
Abstract: The present research explores the utilization of the Mask R-CNN convolutional network in the context of real-time road lane detection and segmentation, with a particular emphasis on its applicability to diverse environmental conditions. Through systematic experimentation and analysis, our study demonstrates the efficacy and adaptability of the proposed framework. The results, as illustrated in accompanying figures, highlight the model's capability to robustly identify and segment road lanes, thus contributing to the enhancement of autonomous vehicle navigation. Importantly, our system's proficiency extends to accommodating varied weather conditions, including scenarios with sunshine, rainfall, overcast skies, and others, as well as its adaptability to both day and night conditions. The real-time decision-making capacity of the system plays a pivotal role in optimizing the maneuvering of autonomous vehicles, offering potential benefits for road safety and traffic management. Moreover, we detail our dataset collection process and thorough pre-processing steps, ensuring the accuracy of model training and evaluation. This research contributes valuable insights into the practical application of deep learning, particularly the Mask R-CNN architecture, within the realm of autonomous driving, with implications for advancing road safety, traffic optimization, and the overall efficiency of autonomous vehicle operations, opening avenues for future refinements and expansions of this approach.
Keywords: lane detection;image processing;computer vision;machine learning;deep learning
Review score: Moderate
Review comment: The reader does not quite understand what the contribution of the authors is, it is necessary to present it in a more explicit form. Improvements are needed in the structure of the manuscript - to more explicitly state the purpose of the study, the tasks, and the results obtained. The above graphic material is borrowed – do not make links.
Title: FMEA variables of software failure risks based on journals and metrics
Abstract: The paper describes the joint application of text comparison model, time series anomaly detection and failure modes and effects analysis (FMEA) to calculate 3 software failure risk scores. The aim of the research is to develop and verify the software failure risk assessment model. The objectives are model development and experimentation. The study allows examining commonly used models from the perspective of their joint application in risk assessments.The study is based on a data set that contains: a list of 24-hour application execution log records with timestamps, severity levels and messages obtained from the Loki log database, a time series for the same period extracted from the Prometheus time series database. To create an initial dataset the test application has been developed which simulates RAM spikes with an error level entry added to the log. The experiment was performed using Kotlin and R languages. The study used Apache Commons Text package to perform text comparison. Time series is passed to the R runtime using the forecast library, a model is formed to detect anomalies and predict "normal" values at outlier points. Thus, a list of points on the time series with anomalous values is obtained and they are associated with log records. As a result of the experiment, a time series graph was obtained with the identified anomalous values and with "normal" values at these points.The final result presents 2 errors: experimental and non-experimental. The experimental error was rated occurrence of 1, detection of 1, severity of 3 and a final rating of 3. The non-experimental error was rated occurrence of 1, detection of 1, severity of 0 and a final rating of 0. It follows that the experimental error was rated non-zero and the non-experimental error was rated as "noise".
Keywords: component;formatting;style;styling
Review score: Moderate
Review comment: The article is interesting. But the following comments need to be corrected:
The quality of the drawings should be improved.
The text and formulas must be adjusted according to the IEEE rules. The abstract needs to be improved. You need to add numerical results to the abstract.
Title: FMEA variables of software failure risks based on journals and metrics
Abstract: The paper describes the joint application of text comparison model, time series anomaly detection and failure modes and effects analysis (FMEA) to calculate 3 software failure risk scores. The aim of the research is to develop and verify the software failure risk assessment model. The objectives are model development and experimentation. The study allows examining commonly used models from the perspective of their joint application in risk assessments.The study is based on a data set that contains: a list of 24-hour application execution log records with timestamps, severity levels and messages obtained from the Loki log database, a time series for the same period extracted from the Prometheus time series database. To create an initial dataset the test application has been developed which simulates RAM spikes with an error level entry added to the log. The experiment was performed using Kotlin and R languages. The study used Apache Commons Text package to perform text comparison. Time series is passed to the R runtime using the forecast library, a model is formed to detect anomalies and predict "normal" values at outlier points. Thus, a list of points on the time series with anomalous values is obtained and they are associated with log records. As a result of the experiment, a time series graph was obtained with the identified anomalous values and with "normal" values at these points.The final result presents 2 errors: experimental and non-experimental. The experimental error was rated occurrence of 1, detection of 1, severity of 3 and a final rating of 3. The non-experimental error was rated occurrence of 1, detection of 1, severity of 0 and a final rating of 0. It follows that the experimental error was rated non-zero and the non-experimental error was rated as "noise".
Keywords: component;formatting;style;styling
Review score: High
Review comment: 1. It is necessary to formulate the scientific novelty of the publication, both in the abstract and in the text of the article. Indicate the goals of the study and its objectives.
2. Formulas are displayed as pictures; this does not meet the requirements for the design of articles. Needs to be corrected according to the template.
3. No link to table 1.
Title: Methodological Approach for Choosing Type of IT Projects Management
Abstract: This paper proposes a methodological approach for selecting an appropriate project management type for IT projects. The approach integrates quantitative assessments derived from specific project characteristics with qualitative criteria, providing a comprehensive decision-making framework. The method involves equations to evaluate project complexity, stakeholder dynamics, risk, and resource availability. A qualitative decision matrix is then introduced, taking into account organizational priorities and preferences. This matrix considers criteria such as adaptability to changes, project team collaboration, documentation requirements, and delivery speed.
Keywords: IT;project;matrix;project management;quantitative assessment;qualitative assessment;methodological approach
Review score: Moderate
Review comment: -
Title: Methodological Approach for Choosing Type of IT Projects Management
Abstract: This paper proposes a methodological approach for selecting an appropriate project management type for IT projects. The approach integrates quantitative assessments derived from specific project characteristics with qualitative criteria, providing a comprehensive decision-making framework. The method involves equations to evaluate project complexity, stakeholder dynamics, risk, and resource availability. A qualitative decision matrix is then introduced, taking into account organizational priorities and preferences. This matrix considers criteria such as adaptability to changes, project team collaboration, documentation requirements, and delivery speed.
Keywords: IT;project;matrix;project management;quantitative assessment;qualitative assessment;methodological approach
Review score: Moderate
Review comment: Accepted.
Title: Methodological Approach for Choosing Type of IT Projects Management
Abstract: This paper proposes a methodological approach for selecting an appropriate project management type for IT projects. The approach integrates quantitative assessments derived from specific project characteristics with qualitative criteria, providing a comprehensive decision-making framework. The method involves equations to evaluate project complexity, stakeholder dynamics, risk, and resource availability. A qualitative decision matrix is then introduced, taking into account organizational priorities and preferences. This matrix considers criteria such as adaptability to changes, project team collaboration, documentation requirements, and delivery speed.
Keywords: IT;project;matrix;project management;quantitative assessment;qualitative assessment;methodological approach
Review score: Moderate
Review comment: The article offers a novel framework relevant to the conference issue, presenting a methodological approach that integrates quantitative and qualitative assessments in IT project management. We recommend a few small changes to improve language clarity and table presentation for better understanding, despite the article's practical application and advancement in the area. The integrated decision matrix is unique and has the potential to impact existing processes. The conclusions reached are logical and provide validity for the usefulness of the approach
Title: Development of Deep Convolutional Neural Network for Road Sign Detection
Abstract: This research paper delves into the realm of road sign detection and classification using deep Convolutional Neural Networks (CNNs). Leveraging the robust capabilities of CNNs, the study comprehensively investigates the intricate landscape of computer vision techniques applied to the task of road sign recognition. The focal point of this research centers on the design and evaluation of a CNN-based architecture trained on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, comprising over 50,000 annotated road sign images across 43 distinct classes. Through a rigorous examination of theoretical foundations and practical implementation, the study elucidates the pivotal role of CNNs in enhancing the accuracy and efficiency of road sign detection systems. The research evaluates the model's performance using precision, recall, and F1-score metrics, consistently demonstrating the model's adeptness in minimizing false positives while effectively capturing pertinent road sign instances. Furthermore, this study underscores the significance of meticulous dataset curation, model optimization, and training procedure refinement in augmenting the efficacy of road sign detection systems. The outcomes of this research have far-reaching implications for intelligent transportation systems, autonomous vehicles, and road safety, offering a promising trajectory towards the advancement of responsive and reliable road sign recognition technologies.
Keywords: road sign detection;image analysis;artificial intelligence;CNN;classification
Review score: Moderate
Review comment: Table 5, which is mentioned on page 5, is not provided. Perhaps you mean table 1?
Are formulas 1-3 missing?
What networks and methods of detecting road signs are used today. It is necessary to specify them in the annotation and compare them with existing ones in the introduction, showing what is the advantage of a neural network for detecting road signs
Title: Development of Deep Convolutional Neural Network for Road Sign Detection
Abstract: This research paper delves into the realm of road sign detection and classification using deep Convolutional Neural Networks (CNNs). Leveraging the robust capabilities of CNNs, the study comprehensively investigates the intricate landscape of computer vision techniques applied to the task of road sign recognition. The focal point of this research centers on the design and evaluation of a CNN-based architecture trained on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, comprising over 50,000 annotated road sign images across 43 distinct classes. Through a rigorous examination of theoretical foundations and practical implementation, the study elucidates the pivotal role of CNNs in enhancing the accuracy and efficiency of road sign detection systems. The research evaluates the model's performance using precision, recall, and F1-score metrics, consistently demonstrating the model's adeptness in minimizing false positives while effectively capturing pertinent road sign instances. Furthermore, this study underscores the significance of meticulous dataset curation, model optimization, and training procedure refinement in augmenting the efficacy of road sign detection systems. The outcomes of this research have far-reaching implications for intelligent transportation systems, autonomous vehicles, and road safety, offering a promising trajectory towards the advancement of responsive and reliable road sign recognition technologies.
Keywords: road sign detection;image analysis;artificial intelligence;CNN;classification
Review score: Low
Review comment: The contribution of the paper is not significant. 
This paper provides a discussion of the development of deep CNN for road sign detection. However, this paper has some critical problems as follow.
Literature review provided does not allow the reader to understand the contribution of the paper with respect to previous published literature. Figure 2 contains labels in Kazakh language instead of English.
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: Thank you for sharing the comprehensive research and development project on speech recognition based on neural network algorithms. It appears that you have conducted a thorough investigation into the performance metrics of speech recognition models and their implications for educational applications. 
1)	Pay attention to the clarity and coherence of your writing, ensuring that sentences are grammatically correct and free of ambiguities. Use concise and precise language to convey your ideas effectively, avoiding unnecessary jargon or technical terms that may confuse readers.
2)	Offer deeper insights into the analysis of your results by discussing the implications of your findings in more detail. Consider addressing potential confounding variables or limitations that may have influenced the outcomes of your study. Additionally, discuss any unexpected findings and their significance in relation to existing literature or theoretical frameworks.
3) Please provide the figures in good resolution.