Artificial Intelligence Components Research Articles

The always changing data problem of using AI in manufacturing : Using synthetic data from the digital twin to feed AI models

Production is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line.

Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics.

Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent "black box" problem of DL models. To evaluate the effectiveness of DL in capturing leaf shape, we used geometric morphometrics (GM), an emerging component of eXplainable Artificial Intelligence (XAI) toolkits. We photographed Ranunculus auricomus leaves directly in situ and after herbarization. From these corresponding leaf images, we automatically extracted DL features using a neural network and digitized leaf shapes using GM. The association between the extracted DL features and GM shapes was then evaluated using dimension reduction and covariation models. DL features facilitated the clustering of leaf images by source populations in both in situ and herbarized leaf image datasets, and certain DL features were significantly associated with biological leaf shape variation as inferred by GM. DL features also enabled leaf classification into morpho-phylogenomic groups within the intricate R. auricomus species complex. We demonstrated that simple in situ leaf imaging and DL reproducibly captured leaf shape variation at the population level, while combining this approach with GM provided key insights into the shape information extracted from images by computer vision, a necessary prerequisite for reliable automated plant phenotyping.

A multi-dimensional hierarchical evaluation system for data quality in trustworthy AI

Recently, the widespread adoption of artificial intelligence (AI) has given rise to a significant trust crisis, stemming from the persistent emergence of issues in practical applications. As a crucial component of AI, data has a profound impact on the trustworthiness of AI. Nevertheless, researchers have struggled with the challenge of rationally assessing data quality, primarily due to the scarcity of versatile and effective evaluation methods. To address this trouble, a multi-dimensional hierarchical evaluation system (MDHES) is proposed to estimate the data quality. Initially, multiple key dimensions are devised to evaluate specific data conditions separately by the calculation of individual scores. Then, the strengths and weaknesses among various dimensions can be provided a clearer understanding. Furthermore, a comprehensive evaluation method, incorporating a fuzzy evaluation model, is developed to synthetically evaluate the data quality. Then, this evaluation method can achieve a dynamic balance, and meanwhile achieve a harmonious integration of subjectivity and objectivity criteria to ensure a more precise assessment result. Finally, rigorous experiment verification and comparison in both benchmark problems and real-world applications demonstrate the effectiveness of the proposed MDHES, which can accurately assess data quality to provide a strong data support for the development of trustworthy AI.

Deployment options of AI components for network resource management in 5G‐enabled agile industrial production cell

SummaryOn‐demand manufacturing in Industry 4.0 requires flexibility of the networks which can be provided with the fifth generation (5G) of mobile communications wireless connectivity. A key component in the efficient utilization of the radio resources in a manufacturing scenario is network resource management (NRM). We show how NRM can be automated with artificial intelligence (AI). We introduce several futuristic industrial use cases that require AI in various parts of the process. We analyze the AI components' benefits and disadvantages in several deployment scenarios. The findings can be used by business stakeholders interested in deploying the 5G cellular wireless network to choose the best NRM and AI implementation strategy for a particular use case. We show that there are many viable options for the AI component in the process automation, but the cost of AI has to be considered in all cases. Also, we point out that an essential component, the standardized information flow on the status of the productivity key performance indicators (KPIs), is needed for the successful deployment and application of the 5G AI.

Predictive Analysis of Crack Growth in Bearings via Neural Networks

Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research work is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables root mean square (RMS), crest factor, signal-to-noise ratio (SNR), skewness, kurtosis, and Shannon entropy were collected from the continuously running bearing and utilized as input parameters, with the total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the Neural Network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width. It was observed that the ANN model performed admirably in predicting data with a better degree of accuracy. Through analysis, it was observed that the SNR was the most relevant parameter in anticipating data in bearing crack propagation, with an accuracy rate of 99.2% when evaluated as a single parameter, whereas in multiple parameter analysis, a combination of kurtosis and Shannon entropy gave a 99.39% accuracy rate.

Artificial Intelligence in Healthcare 5.0: Strengthening Practices for Medicos

Abstract: From the beginning, the Healthcare sector stood a pioneer for the development of Artificial Intelligence technology. Numerous research and discussion have been conducted till today based on the idea of Artificial Intelligence. This is because of the nature of the amenities and the susceptibility of ample portion of consumers. In the present study, a blended approach both qualitative and quantitative has been applied to determine the constituents of Artificial Intelligence for healthcare industry and analysis is conducted to find its effect on value formation along with market performance. By analysis of the patient perspective, it revealed the ways in which different Artificial Intelligence components contribute to healthcare organizations and provide improved patient-centered health care.

Quantum AI for cybersecurity in financial supply chains: Enhancing cryptography using random security generators

Securing the financial supply chains of the United States against sophisticated cyber threats is crucial in the digital age. This research integrates Quantum Random Number Generators (QRNGs) and Artificial Intelligence (AI) to enhance cybersecurity in financial supply chains. QRNGs leverage quantum mechanics to generate truly random numbers, essential for creating secure cryptographic keys, addressing the vulnerabilities of traditional deterministic RNGs. Our results demonstrate the superior performance of the QRNG-based system compared to classical RNG systems. The QRNG showed high entropy and unpredictability, passing NIST SP800-22 and Diehard tests with significantly higher rates. The AI component achieved high precision and recall in detecting cyber threats, enhancing the system's real-time security capabilities. The combined QRNG and AI system exhibited faster encryption and decryption speeds, lower latency, and higher resistance to predictive, quantum, and brute-force attacks. This research underscores the critical importance of adopting QRNGs and AI to secure financial supply chains. The integrated system offers robust cryptographic protection and real-time threat detection, providing a comprehensive solution to mitigate cyber threats. These findings highlight the potential of QRNG and AI technologies to revolutionize cybersecurity in the financial sector, ensuring the integrity and confidentiality of financial transactions and protecting critical financial infrastructure.

Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture

Context:Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks related to the systems’ quality. Such development alone is insufficient to fully address socio-technical consequences and the need for rapid adaptation to evolutionary changes. Recent work proposed the concept of AI technical debt, a potential liability concerned with developing AI-enabled systems whose impact can affect the overall systems’ quality. While the problem of AI technical debt is rapidly gaining the attention of the software engineering research community, scientific knowledge that contributes to understanding and managing the matter is still limited. Objective:In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers’ awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject. Method:We develop a survey study featuring 53 AI practitioners, in which we collect information on the practical prevalence, severity, and impact of AI technical debt issues affecting the code and the architecture other than the strategies applied by practitioners to identify and mitigate them. Results:The key findings of the study reveal the multiple impacts that AI technical debt issues may have on the quality of AI-enabled systems (e.g., the high negative impact that Undeclared consumers has on security, whereas Jumbled Model Architecture can induce the code to be hard to maintain) and the little support practitioners have to deal with them, limited to apply manual effort for identification and refactoring. Conclusion:We conclude the article by distilling lessons learned and actionable insights for researchers.

Artificial intelligence: a new cutting-edge tool in spine surgery.

The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.

Cyber risk assessment of cyber-enabled autonomous cargo vessel

The increasing interest in autonomous ships within the maritime industry is driven by the pursuit of revenue optimization, operational efficiency, safety improvement and going greener. However, the industry’s increasing reliance on emerging technologies for the development of autonomous ships extends the attack surface, leaving the underlying ship systems vulnerable to potential exploitation by malicious actors. In response to these emerging challenges, this research extends an existing cyber risk assessment approach called FMECA-ATT&CK based on failure modes, effects and criticality analysis (FMECA), and the MITRE ATT&CK framework. As a part of our work, we have expanded the FMECA-ATT&CK approach to assessing cyber risks related to systems with artificial intelligence components in cyber-enabled autonomous ships (e.g. autonomous engine monitoring and control). This new capability was developed using the information and semantics encoded in the MITRE ATLAS framework. FMECA-ATT&CK has been adopted due to its comprehensive and adaptable nature and its promising venue for supporting continuous cyber risk assessment. It helps evaluate the cyber risks associated with the complex and state-of-the-art operational technologies on board autonomous ships. The cyber risk assessment approach assists cybersecurity experts in aligning mitigation strategies for the cyber defence of autonomous ships. It also contributes towards advancing overall cybersecurity in the maritime industry and ensures the safe and secure sailing of autonomous ships. Our key findings after applying the proposed approach against a model of an autonomous cargo ship is the identification of the Navigation Situation Awareness System (NSAS) of the ship as being at the highest risk followed by the Autonomous Engine Monitoring and Control (AEMC) system. Additionally, we identified 3 high, 48 medium, and 5776 low risks across 29 components.

Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop.

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

Design, Fabrication and Monitoring of Automated Polyhouse

The agriculture sector faces the challenge of feeding a growing population while dealing with the effects of climate change. With the limited expansion of cropland, agriculture automation has become necessary. Internet of Things (IoT) and Artificial Intelligence (AI) have made significant strides in various industries, including agriculture. These digital technologies offer smart systems that can monitor, control, and visualize farm operations in realtime, approaching human expertise. The potential applications of IoT and AI in agriculture include smart machinery, irrigation systems, pest control, crop monitoring, and more. Automated polyhouses equipped with IoT and AI components can optimize crop conditions, enhancing yield and quality while minimizing climate impacts. Over the past decade, research in this field has matured, transitioning from conceptual stages to implementation. The integration of AI and IoT into agriculture practices poses technical challenges, but understanding these technologies will facilitate their adoption and the development of IoT and AI-based solutions in farming.

Desktop Assistant Based on NLP

Natural Language Processing (NLP) has emerged as a critical component of artificial intelligence, enabling machines to comprehend and interact with human language. This research paper explores the current state of the art in NLP, highlighting recent innovations, trends, and ongoing challenges. It delves into various applications of NLP, discusses the datasets and models that drive advancements, and examines the evaluation metrics used to assess NLP systems. Key innovations such as transformers, pre-trained language models, and transfer learning have revolutionized the field, leading to significant improvements in performance across a variety of tasks. Additionally, the paper addresses the growing emphasis on ethical AI and bias mitigation, as well as the integration of NLP with other AI technologies to create multimodal systems. Applications of NLP in text classification, sentiment analysis, machine translation, conversational agents, and information retrieval are thoroughly examined. The discussion extends to the critical role of benchmark datasets and pre-trained models in driving progress. Furthermore, the paper evaluates the effectiveness of various metrics used to measure the performance of NLP systems. Finally, the future prospects and potential research directions are considered, highlighting the ongoing efforts to push the boundaries of what NLP can achieve in an increasingly interconnected and data-driven world. Keywords Natural language processing . Natural language understanding . Natural language generation

Towards a high-energy efficiency world: Assessing the impact of artificial intelligence on urban energy efficiency

Global warming has increased the frequency of natural disasters, making enhancing energy efficiency an inevitable choice to address the worsening global climate risks. Whether industrial robots, as a crucial component of artificial intelligence, can contribute to improving energy efficiency remains a subject of debate. This study employs the SBM and super-efficiency SBM models to assess the energy efficiency of 262 Chinese cities from 2010 to 2020. We investigate the impact and mechanisms of industrial robots at the city level on energy efficiency. The research findings indicate that the application of industrial robots promotes enhanced energy efficiency, which remains robust after subjecting it to a battery of tests, including SYS-GMM and instrumental variable analyses. However, a significant nonlinear relationship exists between industrial robot utilization and energy efficiency, with diminishing marginal effects. Mechanism analysis reveals that the fundamental mechanisms facilitating this improvement are the digital economy, industrial structural adjustments, and innovations in green technology. Heterogeneity analysis suggests that industrial robots have a more pronounced impact on cities with higher energy efficiency, greater manufacturing levels in resource-dependent areas, and lower population densities. Furthermore, the application of industrial robots can influence the energy efficiency of neighboring regions through spatial spillover effects.

Recent Trends in Machine and Deep Learning for Verbal and Non-verbal Emotion Detection

Abstract: Emotion recognition, both verbal and non-verbal, is a crucial component of artificial intelligence, psychology, and human-computer interaction. Emotion recognition is an integral component that significantly contributes to the improvement of communication and interaction. The research endeavors to conduct a thorough analysis and synthesis of the most recent developments in Deep Learning (DL) and Machine Learning (ML) techniques. Specifically, the study concentrates on the recognition of both verbal and non-verbal emotions. In contrast to previous research concentrated on verbal or non-verbal emotion detection separately, the study attempts to reconcile the gap between the two by demonstrating how ML and DL can be utilized effectively to detect emotions. The study also examines new methods, including multimodal data and integration of contextual information. Additionally, the research has examined the ethical implications and difficulties associated with emotion detection technologies. Findings have also revealed the wide-reaching implications for various sectors, including healthcare, education, customer service, and entertainment, where comprehending human emotions plays a crucial role in enhancing user experience and outcomes. In conclusion, the study provides invaluable knowledge to practitioners and researchers, which may facilitate the development of more advanced and accurate systems.

Multi-hop temporal knowledge graph reasoning with multi-agent reinforcement learning

Knowledge graph (KG) is a key component of artificial intelligence. In recent years, many large-scale knowledge graphs have been produced and put into practical applications. At present, researchers have proposed many methods to reason facts that do not exist in knowledge graphs using the existing information. However, most traditional reasoning methods lack interpretability, and cannot get the reasoning paths. Therefore, the multi-hop path reasoning method of knowledge graph has gradually become a hot spot. This method finds the next relations through the reinforcement learning agent, gets the whole path, and scores the path. Although these multi-hop path reasoning methods make the reasoning method interpretable, these multi-hop path reasoning methods focus on the choice of relations, ignoring the importance of entities in the path reasoning process. Moreover, with the development of the Temporal Knowledge Graph (TKG), traditional multi-hop path reasoning methods cannot effectively process time information. To solve these problems, a multi-hop path reasoning method of temporal knowledge graph based on multi-agent reinforcement learning is proposed, which is named MA-TPath. MA-TPath uses two agents to perform relation selection and entity selection iteratively. Meanwhile, considering the diversity of temporal reasoning paths, we propose a new type of reward function. In MA-TPath, two agents employ the Long Short-Term Memory Networks (LSTM) to capture current results from the environment and output the corresponding action vectors to the environment through activation functions. Experimentally, MA-TPath outperforms all existing models in 4 out of 4 indicators on the YAGO dataset, 3 out of 4 indicators on the GDETL-5 dataset and the ICEWS-250 dataset, and 1 out of 4 indicators on the ICEWS05–15 dataset. Analysis of the temporal reasoning path indicates that MA-TPath not only surpasses other state-of-the-art reasoning methods over four public temporal knowledge graph datasets but provides rationality for results.

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

Artificial Intelligence and Immersive Technologies in Higher Pedagogical Education

The purpose of the study. The problem of using the capabilities of artificial intelligence and immersive technologies in educational programs is being updated. The article is devoted to the analysis of the role, functions and significance of artificial intelligence and immersive learning technologies in the development of an open educational environment and its personal segments formed by subjects of educational relations in the process of preparing graduates of pedagogical universities to change the conditions of modern society. The possibilities of Generative Pretrained Transformers (GPT) and virtual reality in its various manifestations (AR, VR, AVR) are discussed. Their influence on the formation of the personal educational environment of graduates of pedagogical universities is shown.Materials and methods. The method of comparative analysis of scientific publications in the field of artificial intelligence development, immersive learning technologies, and the formation of a personal educational environment is used. The capabilities of a number of GPT services have been experimentally tested. The ideas about the role and functions of new resources in improving the educational process are summarized. The forecast of the integration of artificial intelligence and immersive technologies in pedagogical education is given.The results of the study. It is shown that Generative Pretrained Transformers are able to implement reference and encyclopedic, constructive and creative, analytical, control and training functions in the educational process while observing the ethics of their application. The connection between the development of artificial intelligence and the appearance in the global information network of virtual characters imitating human appearance and behavior in various contexts, and anthropomorphic robots capable of communicating with humans for educational purposes is indicated. Attention is drawn to the inclusion of new means of communication with artificial intelligence in the personal educational environment of the lecturer, which provides him/her with a combination of the functions of a supplier and at the same time a consumer of knowledge. The prospects of the emergence of virtual and anthropomorphic pedagogical agents with artificial intelligence in the education system and in informal communications are assessed. The educational possibilities of augmented reality, virtual reality and augmented virtual reality as means of immersive learning technologies are defined. The pedagogical and technological conditions for the safe use of immersive learning technologies are given. It is noted that the creation of personal educational environments with components of artificial intelligence and immersive technologies makes it possible to organize the processes of teaching and self-education of a lecturer, ensuring the subjective nature of his/her personal and professional growth and mobility in a complicated world.Conclusion. The conclusion is made that the creativity of innovative lecturers, which is visible in their open educational environments, is a resource for overcoming the reproductive nature of the functioning of pre-trained neural networks. The authors believe that positive feedback in the joint evolution of artificial intelligence (AI) tools and personal segments of the open educational space will contribute to the transformation of the existing information society into a learning society. This makes it possible to form a system of open meta-education according to the needs of each individual.

A fuzzy model for the assessment of factors affecting the choice of an institution of higher education

The proposed approach to the formalization of models of development projects and the development of a higher education institution (HEI) provides the possibility of modeling (research), processes of training future specialists and development with the selection of points of origin of projects for the implementation of management tasks. This makes it possible to simplify the description of the main processes of vocational training and to build models for the analysis of improvements through the system of quality and development in the training of specialists in vocational training. In order to conduct higher education diagnostics and form harmonized decisions in higher education development programs in the system of higher education, it is advisable to use programs for the development and management of business processes, the management functions of which are focused on the analysis of the state of the higher education institution and management functions that ensure a successful and less expensive result of higher education development with taking into account the increased requirements of the external environment and the labor market. One of the conceptual provisions of the development of information technologies and education management systems is the further development of information provision in the formation of a single perfect information environment, informatization of education management systems, and evaluation of the quality and effectiveness of training specialists for the production industries, taking into account the requirements of the labor market, production and modern development of science. The proposed technique and method of comprehensive assessment of the quality of education of graduates of higher education institutions with the help of IT on the basis of indicators and indicators of quality, according to quantitative and qualitative factors, their composition, taking into account the needs of production, the requirements of the external and internal and educational environment. Thus, the main technology of education quality management is the most widespread component of artificial intelligence - fuzzy systems. Thanks to its combination with classical models and methods of ensuring the process of management of higher education institutions, it was possible to obtain wider opportunities for working out those parameters that until then had not been considered due to their qualitative nature. It is advisable to include the following input components to the main indicators: finance, training and development, competence, satisfaction, requirements and assessment of higher education institutions, etc.