Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Influence of the State Investment Budget on the Kyrgyz Agricultural Sector Through Data Analysis
Abstract: The purpose of this study is to select and assess factors influencing the gross output of the agricultural sector of Kyrgyzstan, considering the specifics of its agricultural producers. Our ideology is since the core of the agricultural producers of Kyrgyzstan are peasant farms, providing two-thirds of the gross output of the sector, as well as personal subsidiary plots of the population, producing an additional one-third of this product. Methodologically, the work is based on the possibility of constructing a correlation-regression model that allows us to assess the impact of selected factors on the resulting indicator. Modeling and corresponding calculations have shown that in modern conditions the most effective indicators for the gross output of the agricultural sector of Kyrgyzstan are such indicators as per capita income of the population, investments in fixed capital, as well as the state investment program budget. In this connection, the indicator "average per capita income" has the highest positive impact on the result. The analysis methodology we used made it possible to identify the main statistical connections of selected factors with the resulting indicator and created the basis for forecasting their changes and developing appropriate measures to increase the volume of gross output of the country's agricultural sector.
Keywords: agricultural sector;gross output;state investment program budget;correlation;regression;F-test;Durbin-Watson statistic;elasticity
Review score: Moderate
Review comment: The paper meets all the requirements and contains a thorough literature review along with a well-substantiated experimental part. It can be accepted without any revisions.
Title: Influence of the State Investment Budget on the Kyrgyz Agricultural Sector Through Data Analysis
Abstract: The purpose of this study is to select and assess factors influencing the gross output of the agricultural sector of Kyrgyzstan, considering the specifics of its agricultural producers. Our ideology is since the core of the agricultural producers of Kyrgyzstan are peasant farms, providing two-thirds of the gross output of the sector, as well as personal subsidiary plots of the population, producing an additional one-third of this product. Methodologically, the work is based on the possibility of constructing a correlation-regression model that allows us to assess the impact of selected factors on the resulting indicator. Modeling and corresponding calculations have shown that in modern conditions the most effective indicators for the gross output of the agricultural sector of Kyrgyzstan are such indicators as per capita income of the population, investments in fixed capital, as well as the state investment program budget. In this connection, the indicator "average per capita income" has the highest positive impact on the result. The analysis methodology we used made it possible to identify the main statistical connections of selected factors with the resulting indicator and created the basis for forecasting their changes and developing appropriate measures to increase the volume of gross output of the country's agricultural sector.
Keywords: agricultural sector;gross output;state investment program budget;correlation;regression;F-test;Durbin-Watson statistic;elasticity
Review score: Low
Review comment:  the paper demonstrates promise and merit for acceptance into the conference proceedings
Title: Neural Network Threat Detection Systems for Data Breach Protection
Abstract: This study presents a comprehensive investigation into the development of an effective neural network-based threat detection system aimed at identifying and preventing data leaks. Employing a diverse array of Machine Learning methods, including K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and various Gradient Boosting models, this research paper delves into the optimization of cybersecurity measures through advanced analytical and modeling techniques. Central to our approach is the deployment of a deep multilayer perceptron neural network, designed to accurately detect a wide range of cyber threats that could potentially lead to data breaches. The research methodology encompasses analysis and synthesis, systematization, classification, and a detailed comparative analysis to evaluate the efficacy of each model. The outcome of this study is a sophisticated software solution capable of not only detecting complex threat patterns but also enabling the export of results in CSV format for further examination. This paper contributes to the cybersecurity field by showcasing the potential of neural networks and Machine Learning in creating robust, efficient threat detection systems, thereby enhancing data protection strategies against the backdrop of evolving cyber threats.
Keywords: Intrusion Detection Systems;Neural Networks;Deep Learning;Machine Learning;Data Breach
Review score: High
Review comment: In general, I congratulate the authors for their aspirations and for what they have achieved. The importance of the problem of work in today's realities corresponds to the theme of the conference and is relevant and promising. The scientific article is recommended for publication.
Title: Neural Network Threat Detection Systems for Data Breach Protection
Abstract: This study presents a comprehensive investigation into the development of an effective neural network-based threat detection system aimed at identifying and preventing data leaks. Employing a diverse array of Machine Learning methods, including K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and various Gradient Boosting models, this research paper delves into the optimization of cybersecurity measures through advanced analytical and modeling techniques. Central to our approach is the deployment of a deep multilayer perceptron neural network, designed to accurately detect a wide range of cyber threats that could potentially lead to data breaches. The research methodology encompasses analysis and synthesis, systematization, classification, and a detailed comparative analysis to evaluate the efficacy of each model. The outcome of this study is a sophisticated software solution capable of not only detecting complex threat patterns but also enabling the export of results in CSV format for further examination. This paper contributes to the cybersecurity field by showcasing the potential of neural networks and Machine Learning in creating robust, efficient threat detection systems, thereby enhancing data protection strategies against the backdrop of evolving cyber threats.
Keywords: Intrusion Detection Systems;Neural Networks;Deep Learning;Machine Learning;Data Breach
Review score: Moderate
Review comment: This paper discusses a comprehensive investigation into the development of an effective neural network-based threat detection system aimed at identifying and preventing data leaks.  Compared MLs in the study: KNN, Logistic Regression, DecisionTree, RandomForest, SKLearn Gradient Boosting, XGBoost Gradient Boosting, Light Gradient Boosting, AdaBoost, CatBoost, Bernoulli Naive Bayes, Voting, SVC, as well as the developed DMLPNN deep learning model.
Title: Strategies for digital transformation: Case study for Astana IT University
Abstract: This research paper investigates the strategies for digital transformation within higher education institutions, focusing on implementation methods and their impact. By analyzing empirical data and employing descriptive and correlational research techniques, this study explores the complexities and nuances of digital transformation initiatives. The research highlights the importance of clear and well-defined strategies aligned with organizational goals, supportive organizational cultures conducive to innovation, and active employee engagement in facilitating successful digital transformations. Additionally, the study examines the tangible benefits of digital transformation, including increased efficiency and improved student satisfaction. Through a comprehensive examination of digital transformation strategies, this research aims to provide insights that can help higher education institutions navigate the challenges and opportunities presented by technological change. Ultimately, this research contributes to the ongoing discourse surrounding digital transformation in higher education, offering valuable insights for institutions seeking to leverage technology to drive innovation and enhance educational outcomes.
Keywords: Digital transformation;higher education;strategies;implementation;organizational culture;employee engagement;innovation;technology;empirical research;student satisfaction
Review score: Moderate
Review comment: 1. It is necessary to number the formulas in accordance with the requirements
2. References should be cited in accordance with the relevant standards
3. Check the text for spelling errors in the discussion section
4. Add more references to support your arguments
Title: Strategies for digital transformation: Case study for Astana IT University
Abstract: This research paper investigates the strategies for digital transformation within higher education institutions, focusing on implementation methods and their impact. By analyzing empirical data and employing descriptive and correlational research techniques, this study explores the complexities and nuances of digital transformation initiatives. The research highlights the importance of clear and well-defined strategies aligned with organizational goals, supportive organizational cultures conducive to innovation, and active employee engagement in facilitating successful digital transformations. Additionally, the study examines the tangible benefits of digital transformation, including increased efficiency and improved student satisfaction. Through a comprehensive examination of digital transformation strategies, this research aims to provide insights that can help higher education institutions navigate the challenges and opportunities presented by technological change. Ultimately, this research contributes to the ongoing discourse surrounding digital transformation in higher education, offering valuable insights for institutions seeking to leverage technology to drive innovation and enhance educational outcomes.
Keywords: Digital transformation;higher education;strategies;implementation;organizational culture;employee engagement;innovation;technology;empirical research;student satisfaction
Review score: High
Review comment: The study is relevant and could be Accepted - to recommend the article for publication as presented
Title: Strategies for digital transformation: Case study for Astana IT University
Abstract: This research paper investigates the strategies for digital transformation within higher education institutions, focusing on implementation methods and their impact. By analyzing empirical data and employing descriptive and correlational research techniques, this study explores the complexities and nuances of digital transformation initiatives. The research highlights the importance of clear and well-defined strategies aligned with organizational goals, supportive organizational cultures conducive to innovation, and active employee engagement in facilitating successful digital transformations. Additionally, the study examines the tangible benefits of digital transformation, including increased efficiency and improved student satisfaction. Through a comprehensive examination of digital transformation strategies, this research aims to provide insights that can help higher education institutions navigate the challenges and opportunities presented by technological change. Ultimately, this research contributes to the ongoing discourse surrounding digital transformation in higher education, offering valuable insights for institutions seeking to leverage technology to drive innovation and enhance educational outcomes.
Keywords: Digital transformation;higher education;strategies;implementation;organizational culture;employee engagement;innovation;technology;empirical research;student satisfaction
Review score: Moderate
Review comment: Authors analyzed interesting case, which needs further research. Correlation coefficient is quite high but scatter plot does not show such correlation. Minor improvements are needed.
Title: Identifying The Coconut Leaf Disease Using Deep Transfer Learning Approach
Abstract: Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the ’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry.
Keywords: coconut leaf disease;nutrient deficiency;pest attacks;VGG19;MobileNet v2;Inception v3;ResNet50
Review score: Moderate
Review comment: It is recommended that the abstract of the article be improved to more accurately and informatively describe its content
Title: Data Caching in Edge Computing: A Survey
Abstract: As the Internet of Things (IoT) generates ever-increasing data streams, traditional cloud-centric architectures face crippling challenges in network latency, bandwidth consumption, and resource constraints. This paper explores how data caching in edge computing environments emerges as a potent solution, significantly impacting latency reduction, network efficiency, and overall system performance. We comprehensively review the landscape of edge IoT and data caching, analyze caching benefits and complexities, and delve into architectural integration, caching strategies, and algorithms tailored to address specific IoT challenges. Through case studies in chosen application domains, we quantify the performance improvements enabled by effective caching and pave the way for future research exploring novel caching methodologies and optimization techniques in the dynamic world of edge IoT.
Keywords: Edge Computing;IoT;Data Caching;Optimization;Edge-Cloud Synergy;Mobile Crowdsensing
Review score: Low
Review comment: 
The authors have conducted a thorough review of studies on the topic of caching efficiency in IoT. However, the study lacks the authors' own experimental verification of the research hypothesis. It would be advisable to conduct an experiment or simulation modeling to determine cache parameters and their impact on latency and network throughput.
Title: Data Caching in Edge Computing: A Survey
Abstract: As the Internet of Things (IoT) generates ever-increasing data streams, traditional cloud-centric architectures face crippling challenges in network latency, bandwidth consumption, and resource constraints. This paper explores how data caching in edge computing environments emerges as a potent solution, significantly impacting latency reduction, network efficiency, and overall system performance. We comprehensively review the landscape of edge IoT and data caching, analyze caching benefits and complexities, and delve into architectural integration, caching strategies, and algorithms tailored to address specific IoT challenges. Through case studies in chosen application domains, we quantify the performance improvements enabled by effective caching and pave the way for future research exploring novel caching methodologies and optimization techniques in the dynamic world of edge IoT.
Keywords: Edge Computing;IoT;Data Caching;Optimization;Edge-Cloud Synergy;Mobile Crowdsensing
Review score: Moderate
Review comment: This article represents an important study in the field of optimizing edge computing for the Internet of Things (IoT) through data caching. It addresses the current challenges faced by traditional cloud architectures in the context of the increasing volume of data generated by IoT devices and proposes data caching in edge computing environments as an effective solution to reduce network latency, optimize bandwidth usage, and enhance overall system performance.

The novelty of the article lies in its comprehensive exploration of the role of data caching in edge computing for improving the performance and efficiency of IoT data processing. It provides an overview of the current landscape of edge IoT and data caching, discusses the benefits and complexities of caching, and analyzes architectural integration, caching strategies, and algorithms tailored to address specific IoT challenges. Additionally, the article presents case studies in selected application domains, demonstrating performance improvements enabled by effective caching and paving the way for future research to explore novel caching methodologies and optimization techniques in the dynamic world of edge IoT.

This research survey not only highlights the current challenges and new opportunities in the field of data caching at the edge for IoT but also serves as a catalyst for further research and development in this area. By thoroughly analyzing the current state of affairs and identifying promising directions for advancement, the authors of the article deserve recognition for their work in optimizing IoT data management systems.
Title: Security Risks of ML Models: Adverserial Machine Learning
Abstract: Machine learning has emerged as a transformative technology with diverse applications across various sectors. Despite its widespread adoption, the susceptibility of machine learning algorithms to security threats presents a significant challenge. While conventional security risks at hardware and network levels are well-documented, the unique vulnerabilities of machine learning systems require specialized attention.In this paper, we delve into the critical issue of security risks in machine learning, emphasizing the growing concerns surrounding adversarial attacks. To illustrate the practical implications of these security risks, we present a detailed case study on adversarial attacks. By implementing and analyzing an adversarial attack on image classification models, we demonstrate their detrimental effects on model performance and robustness.Our findings underscore the urgent need for proactive measures to safeguard machine learning systems against security threats. By raising awareness and fostering collaboration between researchers and practitioners, we can pave the way for more secure and resilient machine learning frameworks.
Keywords: Information Security;Adversarial Machine Learning;Security Risks in Machine Learning;Machine Learning Risks
Title: The Role of Learning Analytics in Assessing the Effectiveness of Blended Education: A Case Study of Astana IT University
Abstract: The transformation of higher education has brought new forms and approaches to learning to the forefront, highlighting online and blended learning. With the growing popularity of these new learning formats, questions about their effectiveness have arisen, which can be analyzed using learning analytics data. In the Kazakhstani scientific community, interest in learning analytics is in its infancy, reflected in the lack of publications on this topic. The article discusses blended learning at one of the universities in Kazakhstan, namely Astana IT University. The effectiveness of blended learning is analyzed using several methods to address two main research questions. The article includes a comparative analysis of traditional and blended learning formats at the specified university based on data on the overall academic performance of students in four selected socio-humanitarian disciplines from 2021 to 2023. Educational data were obtained from the Modular Object-Oriented Dynamic Learning Environment (Moodle) system. Additionally, the article presents data from a sociological survey among first-year students regarding their perception of the blended learning format. Furthermore, to search for a Pearson linear correlation between online lectures and academic performance data on the number of interactions students have with online content from the Learn system (Open edX) were extracted and analyzed.
Keywords: learning analytics;blended education;higher education;academic performance;Astana IT University
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 is more like a review paper. To make it stronger and acceptable for the IEEE conference, it suggested to include experimental results and technical analysis. Also, it is suggested to consider more up-to-date research literature.