Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Exploring Speech Emotion Recognition with MLP Classifier: A Comprehensive Study on the CREMA-D Dataset
Abstract: Speech emotion recognition is a pivotal aspect of human-computer interaction and affective computing. This research delves into the application of the Multi-Layer Perceptron (MLP) Classifier for emotion recognition within speech, focusing on the widely used CREMA-D dataset. Our investigation covers meticulous pre-processing methods, feature extraction techniques, and model configuration. Raw audio data undergoes preprocessing, with acoustic features like Mel-frequency cepstral coefficients (MFCCs) and pitch-related parameters extracted. The MLP Classifier, a feedforward neural network, is employed to categorize emotions in speech samples. Our results reveal competitive performance in recognizing a spectrum of emotions, including happiness, sadness, anger, and neutrality within the CREMA-D dataset. Our study examines the influence of di-verse hyperparameters, training strategies, and model architectures on emotion recognition accuracy. Cross-validation ensures the reliability of our findings, emphasizing the relevance of dataset selection and the potential for advancing speech emotion recognition research. This research aids in enhancing human-computer interaction systems, sentiment analysis, and emotional well-being monitoring applications, while also contributing to the discourse on optimizing this technology.
Keywords: Speech Emotion Recognition;MLP Classifier;CREMA-D Dataset;Emotion Classification;Acoustic Feature Extraction
Review score: Moderate
Review comment: It looks like paper partly generated by Chatgpt.
Please correct this part.

Also, fix the figures and tables, give information about page 4 and 5.
Title: Exploring Speech Emotion Recognition with MLP Classifier: A Comprehensive Study on the CREMA-D Dataset
Abstract: Speech emotion recognition is a pivotal aspect of human-computer interaction and affective computing. This research delves into the application of the Multi-Layer Perceptron (MLP) Classifier for emotion recognition within speech, focusing on the widely used CREMA-D dataset. Our investigation covers meticulous pre-processing methods, feature extraction techniques, and model configuration. Raw audio data undergoes preprocessing, with acoustic features like Mel-frequency cepstral coefficients (MFCCs) and pitch-related parameters extracted. The MLP Classifier, a feedforward neural network, is employed to categorize emotions in speech samples. Our results reveal competitive performance in recognizing a spectrum of emotions, including happiness, sadness, anger, and neutrality within the CREMA-D dataset. Our study examines the influence of di-verse hyperparameters, training strategies, and model architectures on emotion recognition accuracy. Cross-validation ensures the reliability of our findings, emphasizing the relevance of dataset selection and the potential for advancing speech emotion recognition research. This research aids in enhancing human-computer interaction systems, sentiment analysis, and emotional well-being monitoring applications, while also contributing to the discourse on optimizing this technology.
Keywords: Speech Emotion Recognition;MLP Classifier;CREMA-D Dataset;Emotion Classification;Acoustic Feature Extraction
Review score: High
Review comment: The article meets IEEE standards and can be accepted for the conference.
Title: Exploring Speech Emotion Recognition with MLP Classifier: A Comprehensive Study on the CREMA-D Dataset
Abstract: Speech emotion recognition is a pivotal aspect of human-computer interaction and affective computing. This research delves into the application of the Multi-Layer Perceptron (MLP) Classifier for emotion recognition within speech, focusing on the widely used CREMA-D dataset. Our investigation covers meticulous pre-processing methods, feature extraction techniques, and model configuration. Raw audio data undergoes preprocessing, with acoustic features like Mel-frequency cepstral coefficients (MFCCs) and pitch-related parameters extracted. The MLP Classifier, a feedforward neural network, is employed to categorize emotions in speech samples. Our results reveal competitive performance in recognizing a spectrum of emotions, including happiness, sadness, anger, and neutrality within the CREMA-D dataset. Our study examines the influence of di-verse hyperparameters, training strategies, and model architectures on emotion recognition accuracy. Cross-validation ensures the reliability of our findings, emphasizing the relevance of dataset selection and the potential for advancing speech emotion recognition research. This research aids in enhancing human-computer interaction systems, sentiment analysis, and emotional well-being monitoring applications, while also contributing to the discourse on optimizing this technology.
Keywords: Speech Emotion Recognition;MLP Classifier;CREMA-D Dataset;Emotion Classification;Acoustic Feature Extraction
Review score: Moderate
Review comment: it is suggested to add research results as tables not screenshots. The discussion of the results and conclusion section can be improved adding more details on what was achieved and why.
Title: Analysis of the Transfer Characteristics of Refractive Indices and Nonlinear Effects in Optical Optical Fibers Using the Example of Chromatic Dispersion
Abstract: This article shows the physical basis of dispersion and demonstrates that using a strictly monochromatic laser radiation source for signal transmission in glass fiber is impossible. It was shown that the average dispersion has a negative value, corresponding to the slope of the wavelength curve from the refractive index, and normal dispersion is also shown. Differential changes were taken near the wavelength of 1300 nm, corresponding to the transparency window in the infrared range and widely used in optical telecommunication systems. Considering the fluctuation heterogeneity of the glass fiber structure used in modern systems and the changing gradient refractive index, light waves move in the path at different speeds and arrive at the endpoint at different points in time, creating a time pulse of information transmission. The article contains basic mathematical apparatus - the theory of logarithms, finite and infinitesimal increments, elements of mathematical statistics. The main emphasis is placed on the issues of molecular optics and the interaction of light with different refractive indices of glass fiber.
Keywords: dispersion characteristics;refractive index;gradient light guide;wave packet;group velocity;phase velocity;attenuation coefficient
Review score: Moderate
Review comment: Good paper with details on analysis of the transfer characteristics of refractive indices and nonlinear effects in optical optical fibers using the example of chromatic dispersion. It is suggested to include more details to how initial data was received and how all calculations were performed.
Title: Comparative Analysis of ML Algorithms for Fraud Detection
Abstract: The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss.
Keywords: credit card;fraud detection;random forest;logistics regression;decision tree;machine learning;algorithms;fraudulent transactions
Review score: Low
Review comment: The paper present the comparative analysis of ML algorithms for fraud detection and presents experimental research.

Paper can be accepted after revisions:

The turnitin report states the 39% similarity score. Please, try to fix this. The first score in the report is of particular concern - 10%. Big portions of text (for example, in Introduction are taken from this source even without paraphrase, no citation is provided.

In Main Part, subsection C - proposed technique. It should  be your ideas, your methodology. But all the steps from 1 to 6 are taken from source 1 without even citing it. There are other regions in the paper that use text from various resources without citing them. 

Please, provide a discussion , comparing your results with similar studies. Did they also obtained the same result, that the random forest is the best classifier for such kind of problems?

Please, provide a description of dataset. What is the size, samples , etc.

Please, add details to your abstract, for example, the best accuracy obtained with method indication.

It is not clear why you specify so many metrics but calculate only one of them.
Title: Comparative Analysis of ML Algorithms for Fraud Detection
Abstract: The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss.
Keywords: credit card;fraud detection;random forest;logistics regression;decision tree;machine learning;algorithms;fraudulent transactions
Review score: Moderate
Review comment: The methodology employed, including data collection, preprocessing, analysis, and model development, is well-defined and follows a systematic approach, ensuring the validity and reliability of the results.While the paper mentions the dataset's origin and size, more detailed insights into its features, distribution, and potential challenges (e.g., imbalance) could provide a deeper understanding of the experimental setup.
Title: Candidate Generation for Meal Recommendation System
Abstract: In this paper we overview a recommendation system architecture proposed by Google and adjust it to for our meal recommendation system. Then we conduct an overview on models used for building recommendation systems, as well as a candidate generation for the recommendation system. We then focus on Wide and Deep model as a main model for our candidate generation stage. This choice is driven by its ability to effectively capture both memorization and generalization aspects, providing a well-rounded solution for the diverse nature of meal preferences. To test and validate our choice we then experiment involving training and testing Wide and Deep model. The model is evaluated both in isolation and in comparison, to widely used Singular Value Decomposition algorithm for recommendation system. Our experimental results show the advantages of Wide and Deep model for candidate generation stage of meal recommendation system.
Keywords: component;deep learning;machine learning;recommendation system;wide and depp;meal planning
Review score: Moderate
Review comment: Accept the article without changes
Title: Candidate Generation for Meal Recommendation System
Abstract: In this paper we overview a recommendation system architecture proposed by Google and adjust it to for our meal recommendation system. Then we conduct an overview on models used for building recommendation systems, as well as a candidate generation for the recommendation system. We then focus on Wide and Deep model as a main model for our candidate generation stage. This choice is driven by its ability to effectively capture both memorization and generalization aspects, providing a well-rounded solution for the diverse nature of meal preferences. To test and validate our choice we then experiment involving training and testing Wide and Deep model. The model is evaluated both in isolation and in comparison, to widely used Singular Value Decomposition algorithm for recommendation system. Our experimental results show the advantages of Wide and Deep model for candidate generation stage of meal recommendation system.
Keywords: component;deep learning;machine learning;recommendation system;wide and depp;meal planning
Review score: High
Review comment: This paper offers a notable contribution to the field of recommendation systems, specifically within the domain of meal planning and recommendation. The adaptation of the Wide and Deep model for candidate generation demonstrates both innovation and a solid understanding of the complexities inherent in personalized meal recommendations. Your experiments leveraging the Food.com dataset lend credibility to your claims and showcase the potential for improved accuracy over traditional Singular Value Decomposition methods.
However, for further enhancement and broader application of your findings, I recommend a deeper exploration into the integration of additional dietary preferences and restrictions as features within your model. This could significantly augment the personalized aspect of the recommendations. Additionally, consideration of user feedback loops within your system could offer insights into the dynamic nature of user preferences over time.

Your work stands as a strong contribution to the conference, offering fresh insights and methodologies in the development of recommendation systems. The application of such systems in meal planning is both timely and impactful, given the increasing reliance on digital aids in daily life.
Title: Cognitive Technologies for Object Detection and Topology of the Environment's Information Space
Abstract: The widespread use of ontologies in various fields makes the task of its automated formation quite relevant. The problems associated with its construction within the framework of Data Science and Advanced Analytics are data integration and high-quality information search. Increasing the efficiency of working with ontology is ensured by the connection of cognitive processes with semantic ones. The difference between the analysis, the results of which are presented in this paper, is the cognitive information technologies of the subject area, cognitive and semantic analysis based on the topology of the information space of the environment, which was carried out for the first time. To form a concept in an intellectual environment, the use of the concept's normalization and its features is defined and substantiated. The construction of ontologies is based on the connection of cognitive processes with semantic ones and actualization - description and identification of cognitive information's sources in intellectual environments of subject areas. An approach to formalizing the representation of an information object is considered. The research as a source of information for further formalization and modeling of the information object representation is carried out. The problems that arise in the process of formalizing and analyzing an information object are identified and described. The main difference of the study is the cognitive-semantic analysis based on the theory of mathematical logic and universal algebra, set algebra, tuple algebra and relational algebra, namely: building on open languages of knowledge representation and relations of information sources - tasks - information units.
Keywords: information object;information environment;information source;subject area;cognitive;ontology;concept;semantic model;formalization
Review score: High
Review comment: One little remark: all abbreviations in the text must be opened before their first use. 
The article introduces the concept of Cognitive Information Technology (CIT) software and outlines a method for processing data to acquire essential initial information. The method described offers several advantages, including the ability to incorporate additional features into the search process to refine the classification of CIT software. Furthermore, it allows for the inclusion of various logical or heuristic rules and analytical expressions, which describe the subject area and facilitate the adjustment of CIT software feature values, thereby enhancing problem-solving capabilities.

In conclusion, the study highlights the necessity and relevance of formalizations for Concept of Interest (COI). Additionally, it investigates the factors influencing the formalization of Public Key Infrastructure (PKI). Through the completion of the outlined tasks, the study successfully achieves its objective—within the domain of software ontology and utilizing cognitive information features—to formalize cognitive information technologies and information units.
Title: Cognitive Technologies for Object Detection and Topology of the Environment's Information Space
Abstract: The widespread use of ontologies in various fields makes the task of its automated formation quite relevant. The problems associated with its construction within the framework of Data Science and Advanced Analytics are data integration and high-quality information search. Increasing the efficiency of working with ontology is ensured by the connection of cognitive processes with semantic ones. The difference between the analysis, the results of which are presented in this paper, is the cognitive information technologies of the subject area, cognitive and semantic analysis based on the topology of the information space of the environment, which was carried out for the first time. To form a concept in an intellectual environment, the use of the concept's normalization and its features is defined and substantiated. The construction of ontologies is based on the connection of cognitive processes with semantic ones and actualization - description and identification of cognitive information's sources in intellectual environments of subject areas. An approach to formalizing the representation of an information object is considered. The research as a source of information for further formalization and modeling of the information object representation is carried out. The problems that arise in the process of formalizing and analyzing an information object are identified and described. The main difference of the study is the cognitive-semantic analysis based on the theory of mathematical logic and universal algebra, set algebra, tuple algebra and relational algebra, namely: building on open languages of knowledge representation and relations of information sources - tasks - information units.
Keywords: information object;information environment;information source;subject area;cognitive;ontology;concept;semantic model;formalization
Review score: High
Review comment: The paper's topiс is very relevant but the authors need to improve the structrure of the manuscript. The first section should be Introduction. Formulas should be inserted as formulas not as the pictures. The conclusion is too short. The experimental part is absent, evaluation is absent to, please add more description of experimental work.
Title: Integrated Computer Network Security System: Intrusion Detection and Threat Prediction Using Machine Learning Algorithms
Abstract: The escalating use of computer networks and associated applications has made cybersecurity a critical concern. This research presents a comprehensive approach to addressing network security issues by developing an Intrusion Detection System (IDS) based on the dataset from the Security Information and Event Management System (SIEM) using various machine learning algorithms. The study includes a detailed comparison of models such as Logistic Regression, K-Nearest Neighbors, Gusain Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, XGBoost, and Artificial Neural Networks. The results demonstrated using histograms and tables show the effectiveness of Random Forest and PCA Random Forest, emphasizing their accurate traffic classification. The research evaluates the model’s performance across various cyber-security datasets containing multiple categories of cyber-attacks. It evaluates efficiency by utilizing criteria such as accuracy, precision, and recall. By applying a multilevel approach aligned with the latest trends in machine learning, the study aims to facilitate swift and precise threat analysis and response, ultimately enhancing the overall effectiveness of the cybersecurity system. The study is an effective educational resource that introduces IT students to innovative machine learning and cybersecurity ideas. Through the integration of the results into IT curriculum, educators may close the knowledge gap between theory and practical application by offering students real-world experience in addressing cyber threats. This combined emphasis on innovation and education seeks to develop a new generation of IT professionals with the expertise required for enhanced cybersecurity.
Keywords: Cybersecurity;Machine Learning;Deep Learning;Machine Learning Algorithms;Intrusion Detection System;Artificial Intelligence;Cyber-attack prediction
Review score: Moderate
Review comment: • Lack of novelty: The article does not clearly highlight the novelty or original contribution of the research. It is important to clearly articulate how the proposed integrated computer network security system differs from existing approaches and what new ideas or advances it brings to the field of cybersecurity.
• Limited discussion of data set selection. There is no detailed explanation of why this particular dataset was chosen, how it corresponds to real-world scenarios, and its limitations.
• Discussion of Inadequate Evaluation Metrics: The article briefly mentions precision, precision, and recall as metrics to evaluate model performance. However, it lacks a detailed discussion of the significance of these metrics in the context of intrusion detection and threat prediction using machine learning algorithms. Providing a more in-depth analysis of assessment indicators and their implications could add value to the study.
• There is no verification through actual implementation or practical testing of the proposed integrated security system.