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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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A Machine Learning Approach for Predicting Student Enrollment in the University 30

A Machine Learning Approach for Predicting Student Enrollmen...

引用

30th International Scientific Conference Electronics, ET 2021

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

ISBN: (纸本)9781665445184

The Matura exam is the final national examination that high school students in many countries must pass to be eligible for admission to a university. This paper discusses the key factors that have the most impact in producing a reliable machine learning model for predicting students' enrollment in the university. These factors include the final grades from each high school year, matura exam results and the university entry exam points. It should be noted that demographic factors were not taken into consideration in this study. Four machine learning (ML) techniques with a total of sixteen algorithms were implemented using the Weka software: Bayes (Bayes Net, Naive Bayes, etc.), Logistic Regression (Logistic and SimpleLogistic), K-Nearest Neighbors (IBK, KStar, and LWL) and Decision Tree (J48, Random Forest, RepTree, etc.). According to the results, The RepTree algorithm performed the best with a True Positive (TP) rate of 0.902 and a False Positive (FP) rate of 0.148. The algorithm with the lowest performance was NaiveMulti with a TP rate of only 0.790. However, the range between the worst and the best-performing algorithms was 14.18%. © 2021 IEEE.

关键词： Students

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Healthcare Data Analytics Based on Machine Learning

Healthcare Data Analytics Based on Machine Learning

引用

2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Gancheva, Veska Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies Sofia1000 Bulgaria

ISBN: (纸本)9798350349832

Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they enable the identification and creation of models of complicated datasets and the interactions between them. This, in turn, enables the successful prediction of future outcomes associated with a specific sickness type. An artificial intelligence-based approach to healthcare data analytics is presented, which leverages data to build a desired model and solve a particular issue. The suggested approach for healthcare data analytics uses a random forest and feedforward artificial neural network with two hidden layers as its basis to get the best model. © 2024 IEEE.

关键词： Adversarial machine learning

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Speech recognition with Transformers - Creating and testing datasets for Bulgarian language 30

Speech recognition with Transformers - Creating and testing ...

引用

30th International Scientific Conference Electronics, ET 2021

作者： Mateva, Maya Angelova, Maria Faculty of Computer Systems and Technologies Technical University of Sofia Department of Programming and Computer Technologies Sofia1000 Bulgaria

ISBN: (纸本)9781665445184

Speech is a natural way of communicating between human beings and as such, it triggers an interest of transforming it to a way of interaction with a computer as well. Once it is converted into a sequence of words, it can easily be applied for executing commands, thus enabling people to perform tasks easily or in some cases - tasks that are even impossible for them to be performed. The automatic speech recognition becomes a task solved with various success based on the accuracy, online/offline availability, speed. However, with the constant improvement of the neural networks, it is getting easier to solve and the most prominent solution seems to be the Transformer architecture of neural networks. In this paper, we describe the design of an encoder-decoder TensorFlow architecture and the process of training the model with a specially created Bulgarian dataset. © 2021 IEEE.

关键词： Speech recognition

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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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A recommender system for WordPress themes using item-based collaborative filtering technique 29

A recommender system for WordPress themes using item-based c...

引用

29th International Scientific Conference Electronics, ET 2020

ISBN: (纸本)9781728174266

Recommender systems allow making personalized recommendations for items or products while browsing online, using numerous algorithms and techniques to predict and recommend potentially useful items to a specific user. This paper presents a system built for the purpose of filtering out WordPress themes trough the ratings or reviews of other similar users using item-based collaborative filtering technique and provide user-specific recommendations for the largest online marketplace of web templates called Themeforest, part of Envato marketplaces. © 2020 IEEE.

关键词： Collaborative filtering

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Lookback Period, Epochs and Hidden States Effect on Time Series Prediction Using a LSTM based Neural Network 28

Lookback Period, Epochs and Hidden States Effect on Time Ser...

引用

28th National Conference with International Participation, TELECOM 2020

作者： Koparanov, Kiril Andreev Georgiev, Krasin Krasimirov Shterev, Vasil Aleksandrov 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)9781728187174

Forecasting time series problem occurs in various subject areas. Recently neural network techniques have been used for solving such tasks. However, they have not been sufficiently studied. The article explores the influence of the lookback period, the training epochs, and hidden state dimensionality in forecasting time series using long short-term memory. Numerical experiments with example financial data show that using more lags does not improve the results. Such a study of model parameters is important for their proper selection. © 2020 IEEE.

关键词： Time series

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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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Software Anomaly Detection Method Based on Artificial Neural Network 20

Software Anomaly Detection Method Based on Artificial Neural...

引用

20th IEEE International Conference on e-Business Engineering, ICEBE 2024

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)9798350365856

A growing range of data sets have been created in recent years;these are used by platforms and software applications and kept in remote access repositories. Datasets are therefore more susceptible to harmful attacks. As a result, network security in data transmission is becoming a more important area of study. One well-known method for safeguarding computer systems is the deployment of intrusion detection systems. This study proposes an artificial intelligence based method for data analysis-based anomaly detection. Methods based on machine learning and rules are mixed together. The right rules are created via a genetic algorithm. Relevant features are extracted using principal component analysis with the goal of enhancing performance. The KDD Cup 1999 dataset is used to empirically validate the suggested procedure, satisfying the criterion of using appropriate data. Using the well-known benchmark dataset, the suggested approach is used to identify and examine four different kinds of attacks: Neptune, Ipsweep, Pod, and Teardrop. During the machine learning phase, the data is categorized into categories of attacks and normal behavior after the features set during the training phase are tested. For the purpose of data analysis, the input data is divided into training and testing sets for an artificial neural network. The first 80% of the data are used to train the neural network, and the remaining 20% are used for testing. The estimated accuracy improves with the number of epochs and is higher for training data and lower for validation test data, according to experimental results. Consequently, the trained model can be retained and used to detect discrepancies. The learnt model is used to the system to compute new input parameters that are not used during training or validation. © 2024 IEEE.

关键词： Network intrusion

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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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