Advanced Traveller Information Systems to Optimizing Freight Driver Route Selection

Davidich, N.; Чумаченко, Ігор; Davidich, Yurii; Taisiia, Hanieva; Artsybasheva, Natalia; Tatiana, Melenchuk

doi:10.1109/dese51703.2020.9450763

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…Key in our research is that NumPy can perform operations on arrays of different but compatible dimensions. For example, we can add two arrays with dimensions 5x5 and 5: >>> arr1:np.ndarray = np.arange (1,26,1).reshape ((5, 5) >>> arr1 array([ [ 1,2,3,4,5], [ 6,7,8,9,10], [11,12,13,14,15], [16,17,18,19,20], [21,22,23,24,25]]) >>> arr2:np.ndarray = np.arange (1,5) >>> arr2 array( [1,2,3,4,5]) >>> np.add(arr1, arr2) array ([[ 2, 4, 6, 8, 10], [ 7,9,11,13,15], [12,14,16,18,20], [17,19,21,23,25]...…”

Section: >>> Npreshape(user_arr (-1 1)) Array([[1] [2] [3] [4] [5]])mentioning

confidence: 99%

“…The exponential growth of data in various fields, such as finance [11], healthcare [12], sustainability [13] and social media [14], has posed significant challenges in data storage, processing, and analysis. The ability to quickly and efficiently process large volumes of data is crucial for timely decision-making and gaining insights from the data [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of Python Libraries for Multithreading Data Processing

Krivtsov,

Parfeniuk,

Bazilevych

et al. 2024

A.I.S.

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Topicality. The rapid growth of data in various domains has necessitated the development of efficient tools and libraries for data processing and analysis. Python, a popular programming language for data analysis, offers several libraries, such as NumPy and Numba, for numerical computations. However, there is a lack of comprehensive studies comparing the performance of these libraries across different tasks and data sizes. The aim of the study. This study aims to fill this gap by comparing the performance of Python, NumPy, Numba, and Numba.Cuda across different tasks and data sizes. Additionally, it evaluates the impact of multithreading and GPU utilization on computation speed. Research results. The results indicate that Numba and Numba.Cuda significantly optimizes the performance of Python applications, especially for functions involving loops and array operations. Moreover, GPU and multithreading in Python further enhance computation speed, although with certain limitations and considerations. Conclusion. This study contributes to the field by providing valuable insights into the performance of different Python libraries and the effectiveness of GPU and multithreading in Python, thereby aiding researchers and practitioners in selecting the most suitable tools for their computational needs.

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Section: >>> Npreshape(user_arr (-1 1)) Array([[1] [2] [3] [4] [5]])mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Python Libraries for Multithreading Data Processing

Krivtsov,

Parfeniuk,

Bazilevych

et al. 2024

A.I.S.

View full text Add to dashboard Cite

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“…In the modern world, especially considering the current situation related to the COVID-19 pandemic, the theme of analyzing data on epidemic processes remains extremely relevant and critically important. Data analysis is an essential tool that plays a key role and helps understand the spread of disease [3], identify trends [4], identify risk groups of the population [5], evaluate the effectiveness of control measures [6], imagine the scale of the problem [7], and predict the future development of epidemics [8]. It helps scientists, doctors, and relevant authorities make informed decisions and develop strategies for effective epidemic control [9].…”

Section: Introductionmentioning

confidence: 99%

Information system for assessing the informativeness of an epidemic process features

Bazilevych,

Kyrylenko,

Parfenyuk

et al. 2023

SRIT

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The primary objective of this study is to assess the informativeness of various parameters influencing epidemic processes utilizing the Shannon and Kullback–Leibler methods. These methods were selected based on their foundation in the principles of information theory and their extensive application in machine learning, statistics, and other relevant domains. A comparative analysis was performed between the results acquired from both methods, and an information system was designed to facilitate the uploading of data samples and the calculation of factor informativeness impacting the epidemic processes. The findings revealed that certain features, such as “Chronic lung disease,” “Chronic kidney disease,” and “Weakened immunity,” did not carry significant information for further analysis and hindered the forecasting process, as per the data set examined. The developed information system efficiently supports the assessment of feature informativeness, thereby aiding in the comprehensive analysis of epidemic processes and enabling the visualization of the results. This study contributes to the current body of knowledge by providing specific examples of applying the described algorithmic models, comparing various methods and their outcomes, and developing a supportive tool for analyzing epidemic processes.

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“…The world has accelerated the digitalization of most areas of activity, including healthcare systems [2]. Research related to datadriven medicine is aimed at solving such problems as automated diagnostics [3], analysis of medical [4] and nonmedical interventions [5] to reduce the dynamics of morbidity, analysis of medical images [6], analysis of medical data [7], and modeling the dynamics of the epidemic process [8].…”

Section: Introductionmentioning

confidence: 99%

“…(2) To develop a predictive model for COVID-19 dynamics based on the logistic regression method (3) To develop a predictive model of COVID-19 dynamics based on the decision tree method (4) To develop a predictive model for COVID-19 dynamics based on the support vector regression method (5) To evaluate the results of predicting the dynamics of COVID-19 using the developed models for data in various territories (6) To compare the accuracy and adequacy of the developed models performed with the databases of different countries (7) To analyze the performance of the developed models…”

Section: Introductionmentioning

confidence: 99%

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Chumachenko,

Dudkina,

Yakovlev

et al. 2023

International Journal of Telemedicine and Applications

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This study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, adapting, and interpreting these models. The research constructs three statistical machine learning models to predict the spread of COVID-19 in specific regions and evaluates their performance using real COVID-19 incidence data. The paper presents short-term (3, 7, 14, 21, and 30 days) forecasts of COVID-19 morbidity and mortality for Germany, Japan, South Korea, and Ukraine. The precision of each model was scrutinized based on the type of input data used. Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. The initial findings suggest the need for the comprehensive utilization of all available data, favoring cumulative data during holiday-rich periods and daily data otherwise. To minimize the absolute error, databases should be compiled using daily morbidity and mortality rates.

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Advanced Traveller Information Systems to Optimizing Freight Driver Route Selection

Cited by 12 publications

References 13 publications

Performance Evaluation of Python Libraries for Multithreading Data Processing

Performance Evaluation of Python Libraries for Multithreading Data Processing

Information system for assessing the informativeness of an epidemic process features

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

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