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An Approach to Investigating Plagiarism in Artificial Intelligence Content 12

An Approach to Investigating Plagiarism in Artificial Intell...

引用

12th International Scientific Conference on Computer Science, COMSCI 2024

作者： Minkovska, Daniela Veleva Antonova, Elena Ventseslavova Koparanov, Kiril Andreev Nakov, Plamen Ognyanov Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350392166

A growing number of human-centric tasks like learning, planning, and creative writing require the integration of Artificial Intelligence (AI) in the current era of exponential advancements. Such systems collect and analyze data on their own making the best use of available resources and providing creative solutions for challenging issues. The impact of AI on productivity, data analysis, flexibility, and real-time decision- making in a variety of fields is examined in this paper. Focusing on academic settings, the paper considers ethical concerns surrounding AI-generated content. According to a survey, even though many students use ChatGPT for writing and research, worries about academic integrity and plagiarism still exist. © 2024 IEEE.

关键词： Contrastive Learning

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Understanding Student Success Prediction Using SHapley Additive exPlanations 11

Understanding Student Success Prediction Using SHapley Addit...

引用

11th International Scientific Conference on Computer Science, COMSCI 2023

作者： Ujkani, Berat Minkovska, Daniela Nakov, Ognyan Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350325256

Predicting student success is an important task in educational institutions, as it allows for targeted interventions and support systems to enhance educational outcomes. This paper explores the use of SHAP (SHapley Additive exPlanations) model-agnostic method in understanding and interpreting student success prediction. The predictive model was built using Multi-Layer Perceptron neural network algorithm on a large public dataset. By shedding light on the underlying factors driving student success, this research contributes to the advancement of data-driven decision-making in education. © 2023 IEEE.

关键词： Additives

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Application of Machine Learning Techniques for Software Anomaly Detection 3

Application of Machine Learning Techniques for Software Anom...

引用

3rd International Conference on Applied Mathematics and Computer Science, ICAMCS 2023

作者： Gancheva, Veska Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350324266

A rising variety of platforms and software programs have leveraged repository-stored datasets and remote access in recent years. As a result, datasets are more vulnerable to malicious attacks. As a result, network security has grown in importance as a research topic. The usage of intrusion detection systems is a well-known strategy for safeguarding computer networks. This paper proposes an anomaly detection method that blends rule-based and machine-learning-based methods. In order to construct the appropriate rules, a genetic algorithm is utilized. Principal component analysis is used to extract the relevant features aimed to improve the performance. The suggested method is validated experimentally using the KDD Cup 1999 dataset, which meets the requirement of using appropriate data. The proposed method is applied to detect and analyze four types of attacks in a well-known benchmark dataset: Neptune, Ipsweep, Pod, and Teardrop, utilizing Support Vector Machine, Decision Tree, and Naive Bayes algorithms. After testing the characteristics specified in the training phase, the data is classified into attack categories and normal behavior during the machine learning phase. © 2023 IEEE.

关键词： Network security

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Improving Predictions of Long Sequences by Hyperparameter Tuning 10

Improving Predictions of Long Sequences by Hyperparameter Tu...

引用

10th International Scientific Conference on Computer Science, COMSCI 2022

作者： Koparanov, Kiril Andreev Georgiev, Krasin Krasimirov Shterev, Vasil Aleksandrov Minkovska, Daniela Veleva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Department of Air Transport Faculty of Transport Department of Communication Networks Faculty of Telecommunications Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd. 1000 Sofia Bulgaria

ISBN: (纸本)9781665497770

The problem of forecasting long sequences is important in many different domains. Proper selection of the hyperparameters when a machine learning approach is applied could make the difference between adequate and inadequate model. Several algorithms for automatic hyperparameters tuning were evaluated and compared with baseline selection. As a result, recommendations have been made. Some of the intuitive assumptions for the baseline model proved to be wrong. © 2022 IEEE.

关键词： Forecasting

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The method of dynamic routing in Self-healing Networks 5

The method of dynamic routing in Self-healing Networks

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Anna, Shyman Kuchuk, Nina Kovalenko, Andriy Kashkevich, Svitlana Partyka, Stanislav Yankovskyi, Oleksandr Department of Computer Engineering and Programming National Technical University Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Electronic Computers Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Computerized Management Systems National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350384499

The report proposes a dynamic routing method for Self-healing Networks (ShN). The method takes into account the specific features of ShN. During routing, the flow of service information is reduced. The search for the optimal route is carried out using a convolutional neural network. The method significantly outperforms existing methods when some network nodes fail. ©2024 IEEE.

关键词： Dynamic routing algorithms

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Application of Logistic Regression Technique for Predicting Student Dropout 31

Application of Logistic Regression Technique for Predicting ...

引用

31st International Scientific Conference Electronics, ET 2022

作者： Ujkani, Berat Minkovska, Daniela Stoyanova, Lyudmila Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies Sofia1000 Bulgaria

ISBN: (数字)9781665498784

ISBN: (纸本)9781665498784

Student dropout in higher education is a complex issue and as a process it includes many factors which may affect each other. This paper explores the use and application of a probabilistic supervised machine learning technique for predicting university student dropout to obtain insights on the students at risk and prevent them from dropping out the studies. Data from a public university in the Republic of Kosovo were obtained and examined. The dataset comprises instances of students' dropouts for the past six academic years along with their demographics, grades, enrollment details etc. Logistic Regression, as one of the most widely used Machine Learning and Artificial Intelligence algorithms, was used to build the model and produce the predictions. First, a statistical analysis was conducted and after the data preprocessing, logistic regression classifier was implemented. The results show that a high prediction accuracy was reached, with a percentage of 90%, and a F1 score of 0.85, indicating that the model is performing great and the predictions results are reliable. © 2022 IEEE.

关键词： Students

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Minimal Path Delay Leading Zero Counters on Xilinx FPGAs 23rd

Minimal Path Delay Leading Zero Counters on Xilinx FPGAs

引用

23rd International Conference on Computational Science, ICCS 2023

作者： Morse, Gregory Kozsik, Tam Á S. Rakyta, P É Ter Department of Programming Languages and Compilers Eötvös Loránd University Budapest Hungary Department of Physics of Complex Systems Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9783031360237

We present an improved efficiency Leading Zero Counter for Xilinx FPGAs which improves the path delay while maintaining the resource usage, along with generalizing the scheme to variants whose inputs are of any size. We also show how the Ultrascale architecture also allows for better Intellectual Property solutions of certain forms of this circuit with its newly introduced logic elements. We also present a detailed framework that could be the basis for a methodology to measure results of small-scale circuit designs synthesized via high-level synthesis tools. Our result shows that very high frequencies are achievable with our design, especially at sizes where common applications like floating point addition would require them. For 16, 32 and 64-bit, our real-world build results show a 6%, 14% and 19% path delay improvement respectively, enough of an improvement for large scale designs to have the possibility to operate close to the maximum FPGA supported frequency. ©The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Field programmable gate arrays (FPGA)

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Research into the Influence of Informative Speech Parameters on the Reliability of RNN-based Classification of Human Emotional States 26

Research into the Influence of Informative Speech Parameters...

引用

26th International Conference on Digital Signal Processing and its Applications, DSPA 2024

作者： Alimuradov, Alan K. Tychkov, Alexander Yu. Tyurin, Mikhail A. Dudnikov, Dmitry S. Tychkova, Anna N. Moiseev, Danil S. Myasnikova, Maria G. Student Research and Production Business Incubator Penza State University Penza Russia Penza State University Department of Radio Engineering and Radioelectronic Systems Penza Russia Penza State University Department of Automation and Telemechanics Penza Russia Penza State University Department of Scientific and Innovation Penza Russia Leninogorsk Oil College Department of Information Systems and Programming Leninogorsk Russia

ISBN: (纸本)9798350361728

Reliable classification of human emotional states from speech is dependent on speech signal processing algorithms and classification methods. The reliability is especially affected by the database of emotional speech informative parameters used to train classification methods. The paper is aimed at studying the influence of informative speech signal parameters on classification reliability of human emotional states based on a Recurrent Neural Network (RNN). The research is to expand the information space for emotional speech parameters through the use of an original signal representation algorithm based on Empirical Mode Decomposition (EMD), and assessing the impact on classification reliability using a five-layer RNN. The article outlines the research relevance and provides a brief overview of related academic papers. Local and global informative speech parameters relevant to human emotional states are considered in detail. The emotional speech database is described, and the speech signal representation algorithm based on modified decomposition methods is characterized. The research results on the method for classifying human emotional states based on a five-layer RNN using original and extended databases of emotional speech informative parameters are presented. Reliability assessment was performed by computing the confusion matrix for multi-class classification. The research results have evidenced an increase in classification reliability of human emotional states by an average of 2.66 % through expanding the information space for emotional speech parameters. The reliability ranges from 97.4 % to 98.3 % for the classification of emotional states, and from 95.8 % to 98.2 % for the classification of emotions. © 2024 IEEE.

关键词： Empirical mode decomposition

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METHODS FOR IMPROVING THE QUALITY OF CLASSIFICATION ON IMBALANCED DATA 4

METHODS FOR IMPROVING THE QUALITY OF CLASSIFICATION ON IMBAL...

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Gavrylenko, Svitlana Vladislav, Zozulia Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350395532

The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate methods of balancing classes and to develop a classification method on imbalanced data to increase the level of network security. The methods used are: methods of artificial intelligence, machine learning. The following results were obtained: Class balancing methods based on Undersampling, Oversampling and their combinations were studied. The following methods were chosen for further research: SMOTEENN, SVMSMOTE, BorderlineSMOTE, ADASYN, SMOTE, KMeansSMOTE. The UNSW-NB 15 set was used as the source data, which contains information about the normal functioning of the network and during intrusions. A decision tree based on the CART (Classification And Regression Tree) algorithm was used as the basic classifier. According to the research results, it was found that the use of the SMOTEENN method provides an opportunity to improve the quality of detection of intrusions in the functioning of the network. Conclusions. The scientific novelty of the obtained results lies in the complex use of data balancing methods and the method of data classification based on decision trees to detect intrusions into computer networks, which made it possible to reduce the number of Type II errors. © 2023 IEEE.

关键词： Classification (of information)

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RNN-Based Method for Classifying Natural Human Emotional States from Speech 25

RNN-Based Method for Classifying Natural Human Emotional Sta...

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25th International Conference on Digital Signal Processing and its Applications, DSPA 2023

作者： Alimuradov, Alan K. Tychkov, Alexander Yu. Dudnikov, Dmitry S. Tyurin, Mikhail A. Yuskaev, Marat I. Myasnikova, Maria G. Moiseev, Danil S. Student Research and Production Business Incubator Penza State University Penza Russia Penza State University Department of Radio Engineering and Radioelectronic Systems Penza Russia Penza State University Department of Automation and Telemechanics Penza Russia Leninogorsk Oil College Department of Information Systems and Programming Leninogorsk Russia

ISBN: (纸本)9798350337235

An accuracy of classifying human emotional states from speech has been dependent on emotional speech database, speech signal processing algorithms, and classification methods. Numerous static and dynamic classification techniques are used for emotional state recognition. The paper considers a novel method for classifying natural positive, negative, and neutral human emotional states based on a five-layer Recurrent Neural Network (RNN). The method novelty is due to various neuron activation functions employed for each network layer stemming from the peculiarities of natural emotional speech informative parameters used as input data for the neural network. Local and global informative speech parameters relevant to human emotional states are outlined, and the known classification methods are surveyed. The proposed classification method is described, and the developed Russian database of natural emotional speech is presented. The research results compared with the widely used classification methods have evidenced 95 % accuracy of the developed method, which can be effectively tested in systems for detecting and classifying natural human emotional states from speech. © 2023 IEEE.

关键词： Recurrent neural networks

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