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The article presents the discussion on concept of the industrial metaverse, a digital reality that mirrors and simulates physical systems to optimize processes and drive sustainability in the chemical process industries. Topics include integration of digital twins, the challenges of implementing high-performance computing; and the importance of upskilling the workforce.

The event-related potentials (ERPs) technique represents a newly developed methodology in cognitive neuroscience and has significantly extended the scope of linguistic studies, offering valuable insights into cognitive processes related to language. While extant literature reviews have addressed specific facets of ERP research on language processing, a comprehensive overview of this domain remains notably absent. This study aims to fill this gap by pioneering a mapping-knowledge-domain analysis of ERP research on language processing using Citespace, a visualized bibliometric software. The current study conducted a meticulous survey and evaluation of relevant literature extracted from the Web of Science core collection. Initially, this study outlines the spatial-temporal distribution within this domain. Subsequently, employing document co-citation analysis, keyword co-occurrence analysis, cluster analysis, and burst detection analysis, this study delved deeper into the research landscape. Findings reveal that key areas in ERP research on language processing predominantly focus on sentence comprehension, reading comprehension, and mismatch negativity, with notable emphasis on topics such as speech perception, temporal dynamics, and working memory. The current study advocates for future investigations to concentrate on larger linguistic units, explore the integration of ERP components and their functional significance, and scrutinize individual differences among participants. These directions are imperative for advancing the understanding of language processing mechanisms. [ABSTRACT FROM AUTHOR]

Based on the information construction and taking a hospital as an example, this paper analyzes the current situation, existing problems, and reasons for financial management. This paper puts forward the scheme of optimizing hospital financial management and points out that accounting, asset management, cost management, and performance are all based on accounting. Even though with the continuous improvement and development of hospital informatization, various infrastructures have been built, the problems are more prominent. This can be solved by improving the focus on the products, and processes by reducing waste, or by increasing the quality. In hospitals, the core layer of hospital information technology is the financial management construction and development, and it is also an indispensable factor. It is better to improve the hospital's financial fine management level and promote the hospital's sustainable development. It is not enough to improve the assessment management and comprehensive budget management. It is necessary to integrate the perfect Health Information System, enterprise resource planning system, scientific research system, and structured query language database, establish a comprehensive financial management platform, and obtain data information and indicators of various economic operations in hospitals. Experiments show that the consumables sorting optimization algorithm in this paper can improve the equipment management of medical finance to some extent. In other aspects, this system also incorporates information technology to make enhancements to the original financial management. [ABSTRACT FROM AUTHOR]

Enterprise resource planning (ERP) is widely used to boost the total market power of businesses. The wrong selection is one of the key reasons why ERP installations fail. Due to the complexity of the business environment and the range of ERP systems, choosing an ERP system is a complex and time-consuming procedure. ERP alternatives may be assessed using several criteria, so the ERP selection process may be considered a multicriteria decision-making (MCDM) problem. In this study, the rough best worst method (BWM) was used to determine criteria weights, while the newly developed rough integrated simple weighted sum product (WISP) was used to rank ERP alternatives. Results suggest that the SFT-4 coded software is regarded as the best option, followed by SFT-5, SFT-6, SFT-2, SFT-3, and SFT-1. Results of the newly developed rough WISP method are compared to those of existing rough techniques in the sensitivity analysis. The differences between them have been found to be negligible. The outcomes show how effectively developed rough BWM and WISP integrated method performs in terms of ERP selection with usability, accuracy, ease of use, and consistency. This study will help decision-makers in a context where ERP is implemented choose the best ERP software for different sectors. [ABSTRACT FROM AUTHOR]

Background: At the intersection of neural monitoring and decoding, event- related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application. Methods: We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality. Results: EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT. Conclusion: This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explain how enterprise resource planning (ERP) implementation evolves by cloud computing in different industries with different delivery models of cloud ERP. This paper also investigates infrastructure as a service (IaaS) as a delivery approach for cloud ERP. Case research on IaaS is rarely found in the literature. In addition, this paper intends to reveal how this transformation from on-premises to the cloud would influence the ERP implementation process. Design/methodology/approach: A multiple-case study is conducted to identify the different deployed models of cloud ERP systems in the implementation projects. The influences of emerging cloud computing technology on ERP implementation are investigated by interviewing consultants related to the projects. Findings: The findings illustrate that not only software as a service (SaaS) but also IaaS and platform as a service cloud computing services are widely applied in cloud ERP implementation. This study also indicates that certain technical limitations of cloud ERP might have a positive effect on the outcome of ERP implementation. Originality/value: This study investigates how cloud computing influences ERP implementation from different aspects. The result identifies both SaaS and IaaS as two different approaches widely adopted in cloud ERP implementation. Besides, this study has discussed in-depth and analyzed these two cloud ERP paradigms in five factors, including functionality, performance, portability, security, cost and customization. The classification and suggestions are original to the literature. [ABSTRACT FROM AUTHOR]

Enterprise Resource Planning (ERP) systems have been considered by numerous organizations as a key part of their strategy to support the optimization of their processes and enhancement of their business performance. However, managing business processes through ERP systems and their integration with other systems and data sources is complicated. Part of this difficulty comes from the lack that managers, executives, and implementers have of an integrated and holistic view of ERP systems. This article presents a systemic framework for ERP systems based on many publications as a data sources. It derives from a bibliometric network analysis previously made based on a systematic literature review on ERP major concerns. The methodology used to do the systematic literature review was PRISMA 2020, and the software used to do the bibliometric networking analysis was VOSViewer. The bibliometric analysis was based on the network built based on the previous systematic review and on the clusters that emerged from it. Fifty-nine terms with the highest occurrence were chosen to support six clusters that were found. The data obtained from the bibliometric map, specifically the meaning of each identified cluster, was analysed. These clusters were used as the pillars of the systemic framework proposal. The clusters were used as core areas of the ERP systems field, respectively: (1) infrastructure & technology, (2) leadership & people, (3) processes & performance, (4) implementation & adoption, (5) supplier & implementer, and (6) enterprise & application architecture. The six different clusters and their main relationships were analysed and discussed. [ABSTRACT FROM AUTHOR]

The primary objective of this research study is to assess the impact of technological tools on Chilean companies sales. The most recent data are obtained from the published survey "Acceso y Uso de Tecnología de Información y Comunicación en Empresas." The variables that affect company sales are determined through a descriptive analysis and a linear regression model. The results show that increases in the variables of ICT (Information and Communication Technology) specialists, ERP (Enterprise Resource Planning) software, social networks, broadband, websites, electronic devices, and Big Data generate higher sales for Chilean companies. ERP software and ICT have highest coefficient levels in the regression model. In conclusion, it is recommended that companies opt for the use of digital tools, specifically on ERP systems and in the hiring of specialized ICT personnel. [ABSTRACT FROM AUTHOR]

Context. Considered the task of forming the Master Production Shedule in the process of production management based on the MRP II standard. The object of the study is the algorithm for forming this plan for further planning of materials supply for production and the organization of production itself. Objective. Improvement of the algorithm of Master Production Shedule formation to avoid unnecessary stages of the algorithm. Method. It is proposed to improve the algorithm of the Master Production Shedule formation. It consists in simultaneously taking into account the requirements for timely delivery of products to customers, limitations regarding the capacities of the company’s work centers, and limitations regarding the duration of procurement cycles in the process of supplying materials. The MRP II standard envisages first planning the terms and quantity of product releases, and only at the next step checking the formed plan for admissibility with regard to the required time of operation of the equipment and the availability of the required materials quantity. In case of the calculated plan limitations violation, it is necessary to either plan and implement measures to overcome the specified limitations, i.e. organize additional shifts for work centers, use additional capacities, speed up the delivery of some materials, or reduce the sales plan. All these measures are associated with additional costs. In the proposed version of the planning process, this should be done only if the algorithm does not find an acceptable solution. The task of forming the Master Production Shedule, which is central to the MRP standard, is formulated by the authors as a linear programming task due to the linear nature of the specified restrictions on production capacities and materials. In particular, in the case of sufficiently severe restrictions on the work centers capacity, the plan for replenishing the remaining products from production is shifted to earlier planning intervals and only then rests against the restrictions. Several strategies are proposed for planning replenishments from the production of products stock. Results. The developed algorithms are implemented in the form of Microsoft Excel templates and are available for use in order to deepen the understanding of the MRP II standard. They are also used in the educational process. Conclusions. Approbation of the solution by the authors confirmed its workability, as well as the expediency of implementing the developed modification of the MRP II planning process into the software of leading ERP class systems suppliers. Prospects for further research may consist in a comparative analysis of the proposed options for placement of products replenishment from production, through economic evaluation of these options, as well as through simulation modeling. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to contribute to the scientific debate on Quality 4.0 by exploring the main theoretical themes underpinning the Quality 4.0 model and how the model may be developed. An exploratory sequential mixed methods design was employed to study two different samples of Italian manufacturing companies over two phases. For each sample, a different questionnaire was distributed to the companies' quality managers. As a result, eleven themes were elicited and tested. These themes are related to model development, top management, process mapping, data collection and integration with the enterprise resource planning system, use of artificial intelligence software, machine-to-machine data communication, product identification and traceability, document control and digital skills for quality control staff. A theoretical model for Q4.0 is proposed that encapsulates eleven themes of Q4.0 across three categories- people, process, and technology. Results could be particularly helpful for practitioners who may use them as a guideline for implementing and developing Quality 4.0 in a typical Industry 4.0 environment. [ABSTRACT FROM AUTHOR]

Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements. [ABSTRACT FROM AUTHOR]

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Brain-computer interfaces (BCIs) have a potential to revolutionize humancomputer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs--e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments. [ABSTRACT FROM AUTHOR]

Enterprise Resource Planning (ERP) systems offer various functionalities to support an organization's core functions. However, many anticipated benefits often need to materialize due to business context changes and users' high expectations. Continuous adaptation and improvement are necessary to address user disappointments. This research focuses on ERP systems, exploring key factors influencing the success of their implementation. Recognizing challenges in ERP system implementation, this study provides a comprehensive literature review, identifying essential and contemporary Critical Success Factors (CSFs) influenced by technological advancements. Addressing challenges specific to the wood industry, this research introduces additional industry-adapted CSFs, including industry adaptability, integration with production machinery, effective warehouse management, and supply chain tracking. Furthermore, this paper emphasizes the need for continuous adaptation and improvement of ERP systems, especially in light of current trends and technological achievements. This study recommends a holistic approach, considering traditional or essential CSFs while adapting to new trends. Critical success factors in ERP implementation in the next decade involve considering cloud technology, artificial intelligence and machine learning, data security, mobile access, IoT integration, user experience, and training. The main objective of this paper is to identify the latest CSFs in ERP implementation. This research highlights essential success factors in ERP implementation, and contemporary trends in ERP implementation with a particular focus on the specifics of wood industry. While organizations should aim to maximize the potential of ERP systems, they should also acknowledge the crucial role played by human intervention in the effective and responsible implementation of artificial intelligence. [ABSTRACT FROM AUTHOR]

To investigate current trends and challenges related to supply chain management in Germany, we conduct an interview-based study involving 16 experts. The study focuses on three areas: supply chain transformations, software challenges, and the adoption of emerging technologies. Supply chain transformations are influenced by increasing supply chain complexity, the growing prevalence of e-commerce, and the increased importance allocated by customers to trust and transparency. Complexity is, in part, due to disruptions caused by policy responses to the pandemic and to software challenges in terms of the adaptability of ERP systems and the cybersecurity of cloud-based solutions. This study is relevant for practitioners, as it sheds light on pressing issues in supply chain management, and for researchers as it uncovers potential topics that require further research in the future. [ABSTRACT FROM AUTHOR]

This paper presents the development and implementation of Integrated Multilevel Planning Solution (IMPS) a solution adhering to Industry 4.0 and 5.0 standards. Today, companies face challenges in understanding how new orders would impact existing production plans when there is limited traceability and information flow in their manufacturing process. The digital transformation of the production planning system enables a company to overcome the current challenges; however, to overcome the usual barriers of digital transformation a specialized solution for each company should be developed. IMPS was developed by first understanding the problems in the existing production planning process through a gemba (jap. for "actual place") walk and interviews with stakeholders. The solution was designed with a human-centric approach and consists of seven components (Design System App (DSA), SAP (Systems Applications and Products in Data Processing), Microsoft Project, Microsoft Project Server, The Project Group (TPG) PSLink software, TPG ProjectLink, Tableau, and Smart Digital Assistance), which are well connected and integrated into the existing design. The system is accessible to the end user to find information, as the principles of Industry 5.0 require. A multivariant and multiuser planning capability was achieved with an interconnected Gantt chart of the master project with the ability to drill down into individual projects and custom views for various types of internal users. Most of the production planning solutions found in the literature were optimization-oriented, related to the improvements of the calculation methods within the planning activities in order to achieve a better efficiency of the planning system. Here, the goal was to achieve a system architecture that enabled a unique solution for design-to-order manufacturing without complex interventions into the existing system, which overcomes the most common barriers in Industry 4.0 implementations which are the human resistance to change, high investments, a lack of needed skills and knowledge for its implementation and use, and challenges of the adaptability to the new system. IMPS (ver 1.0) is a hybrid solution for SMEs, which aims to advance their planning system from the most commonly used Excel sheets towards a more advanced system but has financial and knowledge limitations from its implementation of highly complex software (ver. 1.0). [ABSTRACT FROM AUTHOR]

Process mining provides a collection of techniques to gain insights into business processes by analyzing event logs. Organizations can gain various insights into their business processes by using process mining techniques. Such techniques use event logs extracted from relational databases supporting the business process as input. However, extracting event logs is challenging due to the size of the data, and it remains ad-hoc. Existing commercial tools partly support the extraction of event logs, but they are proprietary and focus on the mainstream processes such as Purchase-To-Pay (P2P) and Order-To-Cash (O2C). Moreover, the extracted event logs suffer from well-known deficiency, convergence, and divergence issues. For example, due to convergence events are unintentionally duplicated causing unreliable or confusing performance diagnostics. In this paper, we propose an approach to extract event logs while avoiding the aforementioned issues. More in detail, we extract object-centric event logs by using an abstraction layer of the database, called Graph of Relationships (GoRs), designing blueprints with domain knowledge, and converting the database and blueprint into object-centric event logs.We fully implemented the proposed approach, which can extract object-centric event logs from SAP ERP systems, and evaluate the utility and scalability of the proposed approach. [ABSTRACT FROM AUTHOR]

This article presents a developed and functioning Enterprise Resource Planning (ERP) for the warehouse logistics management, designed for the needs of small and medium-sized companies in Bulgaria. The current ERP system aims to satisfy the needs of a specific transport company - to track and manage the main activities. The main thesis of the present development is the leading role of software innovations in the use of data, their summation and distribution among the company's employees in real time. The design methodology of the developed system includes a study and thorough analysis of customer needs and requirements in order to fully cover the processes in the client company. At the same time, the system is flexible enough to adapt easily to future changes in the customer environment. The article presents the main characteristics of the implemented system, such as ease of use, speed, and intuitive design. As a result of its implementation, the management decision-making processes of both the middle and senior management teams of the company have been significantly supported and facilitated. The developed system has a market implementation in Bulgaria, and is in the process of searching for sales representatives, partners and customers in other countries. [ABSTRACT FROM AUTHOR]

Background: The main objective of the study is to facilitate the development and implementation of customised ERP systems by defining an ontology based on mutually exclusive database schemas for various business needs, resulting from the type of activity and the specificity of the market and industry. The need arises from the contradicting duality of the approach, indicating the need to choose between adapting standard solutions to the requirements of specific customers and developing new systems. Methods: Data on the schemes for processes performed and information needed and produced were collected from seventeen companies. The aggregate and synthesis of the research data was carried out to identify common and area-specific solutions. The SOL concept was introduced and implemented. Results: As a result, an ontology providing a universal approach to the analysis of processes was developed in order to automatically describe business processes by assigning companies to business models. Conclusions: The effect of the work on the indicated stage of the project is the development of a knowledge base containing standard models of the ERP class system built of many components. The solution to the research task is an ontology containing the developed structures and mechanisms to allow one to select the system functionality depending on the problems identified in the enterprises. [ABSTRACT FROM AUTHOR]

An Enterprise Resource Planning (ERP) is a program that integrates all the departments and functions of the entire organization in a single IT system [1]. Nowadays, it is possible to differentiate between two types of ERP systems: proprietary systems and open-source systems. In order to profit from the advantages of open-source systems and overcome the drawbacks of free software, lecturers of the subject Introduction to Firms Information Systems at Universitat Jaume introduced Odoo software in the practical sessions of the course. Using active methodologies, lecturers involve students in the design and implementation of an Odoo ERP for the management of a real business. In order to know the previous background, engagement, motivation, and overall satisfaction with this educational innovation, we carried out an online survey of students. The results of this survey will be presented and suggest future avenues for carrying out actions that contribute to the knowledge of the management of these ERP management systems. In addition, the main implications for business management education will be presented. [ABSTRACT FROM AUTHOR]

Software for large enterprises such as the enterprise resource planning (ERP) is more likely to be developed by a team of software developers where the functional requirements (FRs) are distributed in parallel developers. Therefore, development of pre-requisite FRs must be carefully timed to see which requirement is to be implemented first by assigning priority to some FRs over others, so that FRs can be made available on time to parallel developers. This research proposes a hybrid prioritization technique of minimal spanning trees (MST) and AHP called the spanning analytic hierarchical process (SAHP) for FRs prioritization by exploiting MST capability to prioritize large size software FRs with smaller pairwise comparisons but with more consistent results. Using Numerical Assignment (NA) technique, prioritized FRs from SAHP are assigned to priority groups such that top-priority groups contain high-priority FRs and low-priority groups contain low-priority FRs. low-priority group of FRs are dependent on high-priority groups. As a result, within each priority group, inter-dependencies in FRs are reduced for parallel developers. The proposed technique is evaluated on FRs of ODOO ERP and the results showed that SAHP reduces estimation time of parallel developers as compared to AHP and other techniques. [ABSTRACT FROM AUTHOR]

Blockchain and cloud computing continue to emerge and evolve as important technologies for data management. This has strategic implications for data stored in enterprise resource planning (ERP) systems. We use manufacturing bills of materials (MBOMs) as an example of critical data in ERP. MBOMs define the complex product structures of manufacturing enterprises. We survey five alternatives for storing, controlling, and auditing MBOMs in ERP: Oracle Blockchain Table; Oracle Blockchain Platform Enterprise Edition; Microsoft Azure SQL Database Ledger; SAP HANA Blockchain Ledgers; and IBM Blockchain Platform. We also consider cloud computing without blockchain technologies. We analyze the advantages and disadvantages of each alternative by its effect on the management of MBOMs. Although we use MBOMs in our illustrations, many of the concepts discussed also apply to other critical data stored in ERPs. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G33; M21. [ABSTRACT FROM AUTHOR]

Challenges related to effective supply and demand planning and inventory management impose critical planning issues for many small and medium-sized enterprises (SMEs). In recent years, data-driven methods in machine learning (ML) algorithms have provided beneficial results for many large-scale enterprises (LSE). However, ML applications have not yet been tested in SMEs, leaving a technological gap. Limited recourse capabilities and financial constraints expose the risk of implementing an insufficient enterprise resource planning (ERP) setup, which amplifies the need for additional support systems for data-driven decision-making. We found the forecasts and determination of inventory management policies in SMEs are often based on subjective decisions, which might fail to capture the complexity of achieving performance goals. Our research aims to utilize the leverage of ML models for SMEs within demand and inventory management by considering various key performance indicators (KPI). The research is based on collaboration with a Danish SME that faced issues related to forecasting and inventory planning. We implemented the following ML models: Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Random Forest (RF), Wavelet-ANN (W-ANN), and Wavelet-LSTM (W-LSTM) for forecasting purposes and reinforcement learning approaches, namely Q-learning and Deep Q Network (DQN) for inventory management. Results demonstrate that predictive ML models perform superior concerning the statistical forecasting approaches, but not always if we focus on industrial KPIs. However, when ML models are solely considered, the results indicate careful consideration must be regarded, given that model evaluation can be perceived from an academic and managerial perspective. Secondly, Q-learning is found to yield preferable economic results in terms of inventory planning. The proposed models can serve as an extension to modern ERP systems by offering a data-driven approach to demand and supply planning decision-making. [ABSTRACT FROM AUTHOR]

For organizations, the error-free, consistent, and transparent management of business processes is crucial. Process management relates to the proper handling of data, achievable through enterprise resource planning (ERP) software. This software enables all departments, structured around a central database, to work together. From an organizational perspective, one of the most critical factors for the success of ERP software is its users. Users often require support when using ERP software for various reasons, such as lack of training, experience, or technical issues. Providing this support, whether internally or from external sources, incurs a cost. This study seeks to determine whether a structure can be developed where users can resolve their support requests independently. To achieve this goal, we propose a model for an intelligent user support system with an AI-based process engine for ERP users. Using this model, unstructured user support requests, created in natural language, are transformed into structured forms using natural language processing techniques and then classified using multi-class machine learning algorithms. The process relevant to the identified class is executed to generate a solution for the user's problem. In this study, 1,000 samples of text written in natural language across 10 different categories, based on real data, were used in the learning process. The data were analyzed using machine learning algorithms (K-Nearest Neighbors, Naive Bayes, C4.5, Support Vector Machine, Random Forest, Sequential Minimal Optimization, LibSVM) and deep learning algorithms (Long Short-Term Memory). The best classification was achieved with Sequential Minimal Optimization (SMO) using TF-IDF weighting. The developed chat and process robot helped convert requests or problems into structured forms and provided solutions. In addition to solving the problem through chat or process, technologies such as augmented reality and virtual reality were used. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of cybersecurity for enterprise applications, particularly those running on the SAP platform. Topics discussed include the adoption of the NIST Cybersecurity Framework, mapping its controls to SAP operations using tools like SAP Solution Manager, safeguarding SAP landscapes through proper configuration and compliance, implementing detection controls tailored to SAP operations.