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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Akarçay Pervin, Özlem Yapici Pehlivan, Nimet Weber, Gerhard Wilhelm KTO Karatay University Department of Computer Programming Konya Turkey Selçuk University Department of Statistics Konya Turkey Poznan University of Technology Poznan Poland Middle East Technical University Institute of Applied Mathematics Ankara Turkey

ISBN: (纸本)9789616165617

Heart disease, known as cardiovascular disease has been one of the main causes of death worldwide in recent years. It is affected by various risk factors such as high blood pressure, high cholesterol, diabetes, smoking, obesity, physical inactivity, etc. In this study, Cleveland database of UCI repository of heart disease patients is used. The dataset includes 303 instances and 76 variables which of 14 variables are considered. At first, three different feature selection techniques, namely LASSO, Elastic Net and Adaptive LASSO under specific tuning parameters determined by cross-validation, are applied to reduce the number of independent variables used for the prediction. Then, five different classification techniques, K-nearest neighbor, Support Vector Machine, Decision Tree, Random Forest, and Logistic Regression are performed to predict heart disease. Performances of these methods are measured by different metrics, such as accuracy, sensitivity, specificity, and Matthew correlation coefficient. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Decision trees

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Autonomously Reconfigurable Robot Coupled with Intelligent Vision and Control System for Rapid Runtime Adaptation

Autonomously Reconfigurable Robot Coupled with Intelligent V...

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AI IoT Congress (AIIoT), World

作者： Gvarami Labartkava Nurin Binti Suhaimi Thiago Silva Lev Kirischian Independent Researcher Toronto Canada Department of Computer Programming & Analysis Seneca Polytechnic Toronto Canada Department of Electrical and Computer Engineering Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350387803

ISBN: (纸本)9798350387810

The widespread integration of robotic technology across diverse sector’s necessitates solutions that embody both adaptability and speed. However, existing systems often exhibit a trade-off between these attributes, lacking swift adaptability. This paper introduces a novel self-reconfiguration system that addresses this challenge. Employing a scissor mechanism for rapid adjustment and an intelligent binocular vision driven by computer vision and reinforcement learning algorithms, the proposed system facilitates swift reconfiguration while effectively managing workload. Real-time perception and understanding of the surroundings enable the robot to promptly identify optimal dimensions and adjust its configuration accordingly. This capability empowers the robot to rapidly respond to new tasks without manual intervention, thereby augmenting its versatility and agility. Experimental validation demonstrates the system’s adeptness in navigating passages of varying widths in under a second, underscoring the efficacy of our approach in training the robot to adeptly adapt to diverse environmental conditions.

关键词： Training Runtime Navigation Machine vision Reinforcement learning Manuals Control systems

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Sports programming as a Tool for Regional Development of the Digital Industry and Higher Education 4

Sports Programming as a Tool for Regional Development of the...

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4th International Conference on Technology Enhanced Learning in Higher Education, TELE 2024

作者： Galkin, Alexander Miroshnikov, Artem Sapronov, Daniil Skarzhinskaya, Elena Lipetsk State Technical University Faculty of Automation and Computer Science Lipetsk Russia Lipetsk State Technical University Department of Applied Mathematics Lipetsk Russia The Russian Federation of Sports Programming Lipetsk branch Lipetsk Russia Moscow Financial and Industrial University 'Synergy' Department of Cybersport Moscow Russia

ISBN: (纸本)9798350353532

The article presents the history of the development of sports programming as a sport in Russia. The main stages of the formation of the legislative basis for this sport are given. The rationale and description of the process of training athletes in higher education institutions are given - possible traj ectories in the disciplines of 'algorithmic programming' and 'product programming', the influence of the development of sports programming on improving the quality of IT education. As an example, the organization of training students in the discipline of 'algorithmic programming' at the Lipetsk State Technical University is given. The activities of the Lipetsk regional branch of the All- Russian Sports Federation to popularize this sport including among students are also described. © 2024 IEEE.

关键词： Students

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Sustainable Manufacturing Model with Considering Greenhouse Gas Emission and Screening Process of Imperfect Items Under Stochastic Environment

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International Journal of Applied and Computational Mathematics 2022年第3期8卷 1-21页

作者： Dolai, Madhusudan Manna, Amalesh Kumar Mondal, Shyamal Kumar Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore 721102 India Department of Mathematics C. V. Raman Global University Bhubaneswar 752054 India

In this work, a production model with screening of imperfect product and green house gas emission has been developed. Here, it has been considered that the manufacturing process transfer from in-control situation to out-of-control situation after an uncertain time, which is a random variable. In the out-of-control situation, a part of produced items are imperfect. For this reason, the manufacturer decides to check each item through screening process before sale the perfect items to the market. The screening and production rates are not equal and new type of screening process has been considered here. The imperfect rate depends on the length of out-of-control situation and its random nature. Also, the manufacturer sales the imperfect items by reducing the selling price. The greenhouse gas (GHG) emission tax has been considered here and its rate depends on the production rate. The objective of this study is to find the optimal business and production period, optimal production rate which maximizes the expected average profit of the model. Two numerical examples have been considered to illustrate the feasibility of the model. Finally, the sensitivity analysis have been carried out to get the effect of some important parameters on the optimization problem related to the proposed model. © 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Greenhouse gas emission Imperfect production Price discount Screening

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ANCESTRAL PROTEIN SEQUENCE RECONSTRUCTION USING A TREE-STRUCTURED ORNSTEIN-UHLENBECK VARIATIONAL AUTOENCODER 10

ANCESTRAL PROTEIN SEQUENCE RECONSTRUCTION USING A TREE-STRUC...

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10th International Conference on Learning Representations, ICLR 2022

作者： Moreta, Lys Sanz Rønning, Ola Al-Sibahi, Ahmad Salim Hein, Jotun Theobald, Douglas Hamelryck, Thomas Probabilistic Programming Group PLTC Section University of Copenhagen Copenhagen Denmark Department of Statistics University of Oxford Oxford United Kingdom Brandeis University Biochemistry Department MA United States Probabilistic Programming Group SCARB PLTC Section Department of Biology Computer Science University of Copenhagen Copenhagen Denmark

We introduce a deep generative model for representation learning of biological sequences that, unlike existing models, explicitly represents the evolutionary process. The model makes use of a tree-structured Ornstein-Uhlenbeck process, obtained from a given phylogenetic tree, as an informative prior for a variational autoencoder. We show the model performs well on the task of ancestral sequence reconstruction of single protein families. Our results and ablation studies indicate that the explicit representation of evolution using a suitable tree-structured prior has the potential to improve representation learning of biological sequences considerably. Finally, we briefly discuss extensions of the model to genomic-scale data sets and the case of a latent phylogenetic tree. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Genome

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Univariate & Multivariate Forecasting of Time Series Through a Contemporary Manner

Univariate & Multivariate Forecasting of Time Series Through...

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International Conference on High Technology for Sustainable Development (HiTech)

作者： Kiril Koparanov Daniela Minkovska Elena Antonova Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria Department of Computer Systems Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350358100

ISBN: (纸本)9798350358117

Detailed forecasting of time series is one of the fields where the latest advancements in information technology make significant contributions. Operating with financial data poses noteworthy challenges owing to its inherent nature and substantial impact on economic dynamics within society. This study explores the utilization of automated tools as a set of resources for both unifactorial and multifactorial forecasting, drawing conclusions and providing recommendations.

关键词： Economics Accuracy Time series analysis Predictive models Data models Forecasting Sustainable development Information technology

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of Engineering & Technology 2023.

关键词： Support vector machines

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Study of Methods for Improving the Meta-Algorithm of the Bagging Classifier

Study of Methods for Improving the Meta-Algorithm of the Bag...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Oleksii Hornostal Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

In this paper, we explored approaches that improve the performance of ensemble bagging classifiers for identifying the state of a computer system. The following algorithms are considered: Ensemble pruning, Advanced Voting Algorithms, Dynamic Voting Strategies, Confidence Calibration, Adaptation through Meta-Features and Meta-Learning, which allow improving the voting procedure of the bagging meta-algorithm. Artificial data was generated as initial data, which complicates the classification task and contains an increased amount of noise. In the Google Collab environment, software models of algorithms have been developed, and their quality has been assessed. It was found that the use of the Ensemble pruning and Adaptation through Meta-Features algorithm is the most qualitative. In addition, the Ensemble pruning algorithm reduces the number of basic ensemble classifiers, and, as a result, increases the efficiency of computer system identification. Based on the results of the study, a method for identifying a computer system was proposed through the integrated use of a bagging classifier and an optimization procedure based on the Ensemble pruning algorithm.

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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

Speech recognition with Transformers - Creating and testing ...

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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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