Large Data Processing Research Articles

Artificial Intelligence in Health Professions Education

Artificial Intelligence (AI) is revolutionizing various fields, including Health Professions Education (HPE), with its ability to mimic human problem-solving and decision-making capabilities. Unlike the self-aware AI depicted in movies, current AI systems, like ChatGPT, follow human commands and have shown remarkable growth, reaching one million users within five days. AI’s strengths include processing large data sets rapidly, personalizing learning, automating tasks, enhancing presentations, generating clinical cases, and providing real-time feedback.

ПРОГНОЗИРОВАНИЕ ВЫРУЧКИ БЫСТРОРАСТУЩЕЙ КОМПАНИИ С ИСПОЛЬЗОВАНИЕМ ЛОГИСТИЧЕСКОЙ КРИВОЙ

The aim of the study is to assess the advancement potential of fast-growing companies, to create prerequisites for predicting the gain of such companies. The objective is to identify and forecast the growth points of the country’s economy, which is generally based on the advancement of individual economic entities, namely organizations. The research method for forecasting revenue as the main indicator of fast-growing companies (FGC) is considered. As an approximation, a logistic curve (the Ferhulst curve) is examined, the parameters of which are identified by the least squares method. Open data from financial statements of organizations are applied as a source. This work uses the criterion for distinguishing fast-growing companies; the average annual growth is of at least 50% at current prices. The novelty of the work lies in studying fast-growing organizations using a logistic curve (sigmoid). Approximation parameters are identified. A growth assessment (forecast) for organizations for 2040 is made. The statistical reliability of the selected approximation is established for 714 large and medium-sized fast-growing companies from the sample. The research results state, that a sample of about 900 fast-growing Russian companies is identified using the methods for processing large data sets from 2.5 million organizations. The illustrative calculations are based on actual sample data, highlighting the example of Wildberries in more detail. An assessment of the growth opportunities of the organizations under consideration is made. Studying the applicability of the proposed method using statistical criteria is carried out. The findings show that studying fast-growing organizations’ advancement for a medium or longer period using a logistic curve looks more preferable than applying an exponential one. For the vast majority of such organizations, the approximation of the logistic curve is statistically significant.

Average nucleotide identity-based Staphylococcus aureus strain grouping allows identification of strain-specific genes in the pangenome.

We analyzed the genomic diversity of Staphylococcus aureus, a globally prevalent bacterial species that causes serious infections in humans. Our goal was to build a genetic picture of the different strains of S. aureus and which genes may be associated with them. We reprocessed >84,000 genomes and subsampled to remove redundancy. We found that individual samples sharing >99.5% of their genome could be grouped into strains. We also showed that a portion of genes that are present in intermediate frequency in the species are strongly associated with some strains but completely absent from others, suggesting a role in strain specificity. This work lays the foundation for understanding individual gene histories of the S. aureus species and also outlines strategies for processing large bacterial genomic data sets.

Improving the Information and Legal Support of the Judicial System of Ukraine: Experience of the European Court of Human Rights

The research proposes a comprehensive approach to modernizing the information and legal support (ILS) of the judicial system of Ukraine, given European integration processes and the war of Russia against Ukraine. The need for substantial modernization of existing court automation systems to integrate with European systems and improve analytical capabilities for processing large data sets was identified. It was specified that the legal framework for the functioning of court information systems (IS) must be adapted to European legislation, and the organization of data warehouses and information flows must comply with EU standards. The principles of the functioning of court information systems should ensure the possibility of easy integration with the IS of the EU. To investigate war crimes committed by Russia in Ukraine, it is proposed to establish reliable ILS, given the case law of the European Court of Human Rights (ECHR) in cases of human rights violations during conflicts. An innovative approach based on the use of artificial intelligence (AI) tools is proposed to increase the efficiency of judicial decision analysis and improve the ILS of the judicial system of Ukraine. It has been proven that the integration of the judicial systems of Ukraine and the EU is possible, provided there are reliable data analysis tools, process standardization, digitalization, data security, and integrated registers. The effectiveness of applying IT tools in the ILS of courts has been confirmed. It is proposed to establish specialized hybrid courts in Ukraine with international experts to address war crimes, harmonize legislation with international law, and implement the case law of the ECHR.

Research on proportional valve spool wear diagnostic method hybrid-driven by the valve port energy loss mechanism model and data

High performance proportional valves are commonly utilized for precise control of aircraft actuators to ensure flight safety. Fault diagnosis play a crucial role in maintaining equipment reliability. However, traditional diagnostic methods using vibration signals face challenges such as inability to directly measure failure points, fault characteristics being influenced by sensor position, and large data processing volume, limiting engineering application scope. This paper introduces a valve spool wear fault diagnostic method based on energy loss model and data hybrid drive, leveraging throttling loss characteristics. An energy loss failure mechanism model, incorporating differential pressure and flow rate of valve port, was established, and experimentally verified. This method effectively addresses the limitations of vibration sensors in diagnosing aviation hydraulic systems. By combining particle swarm optimization algorithm with deep extreme learning machine, the number of neurons, weight distribution, and data set ratio are rapidly optimized, reducing data processing complexity and enhancing diagnostic efficiency. Comparative tests demonstrate a significant increase in average diagnostic accuracy rate, with more than 8% improvement after integrating energy loss information and particle swarm optimization, reaching over 98%. The proposed method exhibits superior performance in average diagnostic accuracy rate and stability compared to other methods and can be served as a valuable reference for predicting faults in aviation hydraulic valves.

Unmasking The Enigma:Exploring Personality Traits Using Social Media Text

In this paper personality is an important parameter as it differentiates various individuals from another. Predicting personality has many applications in real world. The main objective of this project is to take textual data as input from the user and then run the trained machine learning model on this data to predict his four personality traits which are Introversion vs Extroversion, Sensing vs Intuition, Thinking vs Feeling, Judging vs Perceiving.The main objective is to build an application where users can answer the questions which are processed and analyzed to output his personality traits. The output is a string of four characters where each character determines a personality trait, total of sixteen personality types are possible.The Machine learning model Random Forest Classifier is used to classify the text and output four personality traits. Processing of large textual data is to be done using Natural Language Processing (NLP) techniques with the help of NLTK libraries to process and categorize the data. In order to increase the performance of the model hyper parameter tuning along with cross fold validations is done. Keywords: Random Forest, Natural Language Processing, Text

A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality

Background and objectiveMagnetic particle imaging (MPI) is an emerging imaging technology in medical tomography that utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the in vivo spatial distribution of nanoparticle contrast agents. The reconstruction image quality of MPI is determined by the characteristics of magnetic particles, the setting of the MPI scanner parameters, and the hardware interference of MPI systems. We explore a feasible method to systematically and quickly analyze the impact of these factors on MPI reconstruction image quality. MethodsWe propose a systematic 3-D MPI simulation model. The MPI simulation model has the capability of quickly producing the simulated reconstruction images of a scanned phantom, and quantitative analysis of MPI reconstruction image quality can be achieved by comparing the differences between the input image and output image. These factors are mainly classified as imaging parameters and interference parameters in our model. In order to reduce the computational time of the simulation model, we introduce GPU parallel programming to accelerate the processing of large complex matrix data. For ease of use, we also construct a reliable, high-performance, and open-source 3-D MPI simulation software tool based on our model. The efficiency of our model is evaluated by using OpenMPIData. To demonstrate the capabilities of our model, we conduct simulation experiments using parameters consistent with a real MPI scanner for improving MPI image quality. ResultsThe experimental results show that our simulation model can systematically and quickly evaluate the impact of imaging parameters and interference parameters on MPI reconstruction image quality. ConclusionsWe developed an easy-to-use and open-source 3-D MPI simulation software tool based on our simulation model incorporating all the stages of MPI formation, from signal acquisition to image reconstruction. In the future, our simulation model has potential guiding significance to practical MPI images.

Sparse Fast Fourier Transform Analysis of Aero-Engine Vibration Data

Sparse fast Fourier transform is a new spectrum analysis method based on signal sparse characteristics in recent years. Sparse fast Fourier transform can quickly and accurately process large data by identifying and discarding frequency signals that have no effect on the analysis results. In order to explore the practicality of Sparse fast Fourier transform in aero-engine vibration data analysis, this paper combines the actual flight test vibration data, and studies the sparse fast Fourier transform method and the traditional fast Fourier transform method in terms of spectral characteristics, running time, algorithm stability. By comparison, it can be found that the sparse fast Fourier transform algorithm can greatly improve the efficiency of vibration data analysis while ensuring accuracy.

Reconfigurable nonlocal thin film nano-cavity for image processing

A combination of optical analog computing and nanophotonic design is attractive due to its fast-processing times, large data processing rates, and high integration with modern imaging systems. However, most of the proposed nano-photonics designs for this purpose have static functionality, which limits their applicability. By modulating the Green's function response of the thin film nanocavity using phase change properties of Sb2S3, we propose tunable bright and dark-field imaging. We optimize the nano-cavity for the bright field in a crystalline and dark field in the amorphous phase of Sb2S3. The proposed design shows a near-unity numerical aperture which enables the resolution of the system to ∼500 nm. Lastly, we examine the potential integration of this cavity into a standard interferometric setup and scattering microscopy, leveraging the tunable modulation capability of the proposed cavity as a substrate in reflection mode scheme. This ultrathin, on-chip, and real-time tunable lithographic-free imaging system can play a crucial role in a host of applications such as machine vision, medical imaging, and sensing.

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets

Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub (https://github.com/phipsonlab/SuperCellCyto) and Zenodo (https://doi.org/10.5281/zenodo.10521294).

Application of generalised atomic wavelets in image processing problems

The problem of processing large data arrays is considered. Development of effective algorithms is the basis of successful application of new technologies. Complexity of the algorithm and accuracy of the results are the key quality indicators. The use of compact carrier functions leads to a decrease in processing time and complexity of the numerical algorithm. The accuracy of the data representation and the correctness of the results mainly depend on the approximation properties. It follows that a combination of the above characteristics is necessary to create efficient algorithms for processing large data sets. Aim of article – to develop and evaluate the possibilities of application of generalised wavelets based on Kravchenko-Rvachev functions for image processing with a given quality on the basis of the analysis of the quality loss control mechanism. Discrete image compression by Kravchenko-Rvachev functions possessing such fundamental qualities as good approximation properties, high order of smoothness and existence of a basis with a local carrier in spaces of atomic functions is considered. Since the generalised up-functions have the same properties, similar compression results can be obtained using the generalised discrete atomic transform, which is based on their application, which is a promising direction in digital image processing. The presented method of image processing can be used for various purposes, including reducing the size of data before their transmission through the communication channel from the sensor to the point of reception via a communication line with limited bandwidth, for storing the received images for their further use in the centres of information processing of remote sensing of the earth, as well as for data transmission to potential users.

IN-MEMORY INTELLIGENT COMPUTING

Context. Processed big data has social significance for the development of society and industry. Intelligent processing of big data is a condition for creating a collective mind of a social group, company, state and the planet as a whole. At the same time, the economy of big data (Data Economy) takes first place in the evaluation of processing mechanisms, since two parameters are very important: speed of data processing and energy consumption. Therefore, mechanisms focused on parallel processing of large data within the data storage center will always be in demand on the IT market.
 Objective. The goal of the investigation is to increase the economy of big data (Data Economy) thanks to the analysis of data as truth table addresses for the identification of patterns of production functionalities based on the similarity-difference metric.
 Method. Intelligent computing architectures are proposed for managing cyber-social processes based on monitoring and analysis of big data. It is proposed to process big data as truth table addresses to solve the problems of identification, clustering, and classification of patterns of social and production processes. A family of automata is offered for the analysis of big data, such as addresses. The truth table is considered as a reasonable form of explicit data structures that have a useful constant – a standard address routing order. The goal of processing big data is to make it structured using a truth table for further identification before making actuator decisions. The truth table is considered as a mechanism for parallel structuring and packing of large data in its column to determine their similarity-difference and to equate data at the same addresses. Representation of data as addresses is associated with unitary encoding of patterns by binary vectors on the found universe of primitive data. The mechanism is focused on processorless data processing based on read-write transactions using in-memory computing technology with significant time and energy savings. The metric of truth table big data processing is parallelism, technological simplicity, and linear computational complexity. The price for such advantages is the exponential memory costs of storing explicit structured data.
 Results. Parallel algorithms of in-memory computing are proposed for economic mechanisms of transformation of large unstructured data, such as addresses, into useful structured data. An in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed. It includes a framework for matrix analysis of big data to determine the similarity between vectors that are input to the matrix sequencer. Vector data analysis is transformed into matrix computing for big data processing. The speed of the parallel algorithm for the analysis of big data on the MDV matrix of deductive vectors is linearly dependent on the number of bits of the input vectors or the power of the universe of primitives. A method of identifying patterns using key words has been developed. It is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process. This allows you to use read-write transactions for parallel processing of large data such as addresses.
 Conclusions. The scientific novelty consists in the development of the following innovative solutions: 1) a new vector-matrix technology for parallel processing of large data, such as addresses, is proposed, characterized by the use of read-write transactions on matrix memory without the use of processor logic; 2) an in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed; 3) a method of identifying patterns using keywords is proposed, which is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process, which makes it possible to use the read-write transaction for parallel processing of large data such as addresses. The practical significance of the study is that any task of artificial intelligence (similarity-difference, classification-clustering and recognition, pattern identification) can be solved technologically simply and efficiently with the help of a truth table (or its derivatives) and unitarily coded big data . Research prospects are related to the implementation of this digital modeling technology devices on the EDA market. KEYWORDS: Intelligent

MANAGEMENT OF INNOVATION PROCESSES

Irma Chkhaidze E-mail: chkhaidze.irma@bsu.edu.ge Associate Professor, Batumi Shota Rustaveli State University Batumi, Georgia https://orcid.org/000-0003-4430-181X Lela Devadze E-mail: devadzelela@gmail.com Doctoral student in Business Administration, Assistant, Batumi Shota Rustaveli State University Batumi, Georgia https://orcid.org/0000-0002-1900-8024 Abstract. In the article, it has been studied models of “1G-Technology Push”, 2G-attracting the market”, “3G-R&D and market coupling”,“4G-Integrated Business Processes”,“5G-system integration and network” on the one hand. Consecutive phases of failure and development of the essence of artificial intelligence from its origin to the present day on the other hand. It`s been analyzed the convergence of the development of innovation process models with the phases of formation and development of artificial intelligence. “6G-open innovation model” of innovation process has been reviewed as an actual issued in the article, the formation and development of which is related to the period of popularization of artificial intelligence, and recognition of the advantages of artificial intelligence, which takes into account the generation and rapid processing of large and varied data in a short time interval. Based on the results of research, existing and expecting trends of the development of artificial intelligence in the world market has been analyzed, which are characterized by significant growth trends and can have a positive impact on achieving the rates of economic growth of the country, region, on increasing the level of employment, developing the business sector, gaining competitiveness. The research determined the existing level of development of artificial intelligence, factors, conditions and opportunities for hindering the development. Keywords: innovation, innovative processes, artificial intelligence, management. JEL classification: O.03.032.

A Classification Algorithm Inspired by the Chromatographic Separation Mechanism Dedicated to the Classification of Variable-length and Multi-class Vectors

Nowadays, one of the critical problems related to data mining is the processing of large data sets. This article presents an algorithm that may apply to the issues associated with classifying large-volume data sets. The motivation behind defining this type of algorithm was that the methods used to process this data type are subject to several significant limitations. The first considerable limitation of using classical classification methods is ensuring a constant data size. The second type of constraint is related to the data dimension. The last limitation in using classic classification algorithms is associated with the situation in which a given input vector may contain data belonging to many classes simultaneously, in which case we are talking about so-called multi-class vectors. The presented algorithm is inspired by the method of chromatographic separation of chemical substances. This method is widely and successfully used in analytical chemistry. As we know, in the case of chromatographic separation, we are dealing with a similar class of problems that occur when processing large data sets, firstly: the molecules of a chemical substance have a different number of molecules - i.e., they have different lengths, which corresponds to the situation that occurs when processing large data sets. In this work, a classification algorithm inspired by the mechanism of resolution chromatography is presented. The article presents the results of calculations for sample data sets. It discusses issues related to the properties of the defined algorithm, which concern the algorithm training process and the classification of single-class and multi-class data.

ANALYSIS OF DATA ORGANIZATION SYSTEM ARCHITECTURES OF THE SMART EUROPEAN REGION "CENTER OF EUROPE" BASED ON THE DATA LAKE TECHNOLOGICAL SOLUTION

In the contemporary landscape of digital transformation, the concept of a "smart region" emerges as a key strategy for regional development, utilizing data integration technologies to enhance the quality of life, optimize service delivery, and promote sustainable growth. The designation of the "Centre of Europe" as a smart region is justified by its strategic geographical location, encompassing Transcarpathia and adjacent areas of neighboring countries, coupled with a relatively low population density. This unique context creates compelling premises for rethinking traditional urban frameworks of smart cities on a broader scale, transforming them into a comprehensive smart region model tailored to the specific needs and opportunities of Central Europe. The rationale for implementing the "Centre of Europe" as a smart region stems from its advantageous position at the heart of the continent, which generates unprecedented opportunities for cross-border collaboration, innovation, and connectivity. The low population density of the region further highlights the potential for the adoption of advanced technological solutions capable of addressing a wide array of challenges and needs. By achieving the status of a smart region, the "Centre of Europe" aspires to become a leading example of how digital and technological innovations can be utilized to create an inclusive, efficient, and progressive regional ecosystem. Objective: The primary objective of this research is to conduct a comparative analysis of advanced data organization system architectures and their role in facilitating the transformation of the "Centre of Europe" into an intelligent European region. It also aims to explore the capabilities of utilizing data lakes for the effective management of large data sets and regional development, taking into account the unique geographical and demographic characteristics of the region. The subject of the study encompasses the architectures of big data organization systems in the smart region of the "Centre of Europe". The object of the study involves the processes of storing and processing large data within the smart region. Main characteristics: This article illuminates the main aspects of implementing big data processing technologies in the smart region of the "Centre of Europe", necessitating scalable and flexible solutions for large data storage, the use of efficient data management practices, and the integration of advanced technologies such as cloud computing, the Internet of Things (IoT), and artificial intelligence (AI) to enhance data processing and analytical capabilities. Furthermore, recommendations for regional authorities on adopting these technologies have been formulated, highlighting the importance of collaboration, support for investments in the region's digital infrastructure as key components for achieving the smart region vision. The construction of modern, high-tech data storage facilities using the Data Lake architecture enables the simultaneous use and processing of large volumes of data from various fields and sources. The authors have proposed conceptual models for forming data complexes from different functional areas of the smart region.

Дослідження проблем синхронізації та захисту даних при реалізації багатопоточних програм

The issue of shared data usage by threads is especially relevant in modern multi-core and multiprocessor systems. The main problems of implementing multithreaded programs are race conditions, deadlocks, and thread starvation. The aim of the work is to solve the problem of thread racing in multithreaded calculations of resource-intensive tasks with parallel access to shared data using appropriate synchronization mechanisms, such as mutexes. A multithreaded algorithm for implementing a typical task of processing large data arrays with protection of the critical area in concurrent programs running on multiprocessor and multi-core systems has been developed and researched.

Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images.

In the context of climate change, monitoring the spatial and temporal variability of plant physiological parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability. Additionally, the application of machine learning techniques, essential for processing large data volumes, has seen a significant rise in agricultural applications. This research was focused on carob tree, a drought-resistant tree crop spread through the Mediterranean basin. The study aimed to develop robust models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of different irrigation systems. Planet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional multiple linear regression. The findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy in predicting net assimilation (R² = 0.81) and stomatal conductance (R² = 0.70), with the yellow and red spectral regions being particularly influential. Furthermore, the research indicates no significant difference in intrinsic water use efficiency between the various irrigation systems and rainfed conditions. This work highlighted the potential of combining satellite remote sensing and machine learning in precision agriculture, with the goal of the efficient monitoring of physiological parameters.

Neural network big data fusion in remote sensing image processing technology

Abstract Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.

Radiomics: A Powerful Tool

The Artificial Intelligence based technique of radiomics is an evolving modality that employs data characterization algorithms to extract a variety of useful features from medical images [1].Tumoral patterns are revealed that cannot be appreciated by the naked eye [2]. This can aid in assessing prognosis and gauging the response of tumor cells to therapy [3]. Various imaging modalities like CT, PET MR provide essential raw data. Extraction tools then use the raw data volumes to analyze pixel/voxel characteristics. Using these images, “volumes of interest” can be generated since such segmentation entails handling and processing of large image data; automatic and semiautomatic segmentation algorithms are employed enabling automation [4]. However, thorough testing and quality assurance are vital to ensure that the algorithm used is not only accurate, consistent, and reproducible but also time-efficient [5].

Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family.

The Flaviviridae family consists of single-stranded positive-sense RNA viruses, which contains the genera Flavivirus, Hepacivirus, Pegivirus, and Pestivirus. Currently, there is an outbreak of viral diseases caused by this family affecting millions of people worldwide, leading to significant morbidity and mortality rates. Advances in computational chemistry have greatly facilitated the discovery of novel drugs and treatments for diseases associated with this family. Chemoinformatic techniques, such as the perturbation theory machine learning method, have played a crucial role in developing new approaches based on ML models that can effectively aid drug discovery. The IFPTML models have shown its capability to handle, classify, and process large data sets with high specificity. The results obtained from different models indicates that this methodology is proficient in processing the data, resulting in a reduction of the false positive rate by 4.25%, along with an accuracy of 83% and reliability of 92%. These values suggest that the model can serve as a computational tool in assisting drug discovery efforts and the development of new treatments against Flaviviridae family diseases.