Process Support System Research Articles

Possibilities of Using Integrated Intelligence in Managing Educational Processes in Higher Education Institutions

Abstract. Problem. In light of modern innovations in the field of higher education, the task of creating a model of a system for supporting educational processes is one of the urgent tasks of digitalization of education, and is considered not only as a task of developing the practice of strategic management in universities, but also as a system for supporting decision-making to regulate the experience and expectations of students. At the same time, there are many promising technologies based on the use of integrated intelligence to improve the teaching and learning process and ensure quality education. However, it has not yet been clearly defined which specific tasks for organizing the learning process can be effectively delegated to models endowed with artificial intelligence. Goal. The goal is to justify the delegation of the performance of individual components of the work of university departments to an artificial intelligence system. Methodology. Methods of analysis, synthesis and decomposition were used to identify the constituent components of the university division's work. The method of graphical modeling was used to construct the structural and functional diagram of the university division. When formalizing the work of the university department as a complex system, quantitative and qualitative evaluation methods were used. Results.The conducted analysis of the department's work determines the directions of formalization of the department's work processes with the aim of intellectualization of work processes based on integrated intelligent systems. The concept of creating a support system for the department's educational processes based on integrated intelligence is proposed to optimize the department's work and improve the quality of education. Originality. A system of support of educational processes (SSEP) based on integrated intelligence is proposed. The basis of the SSEP of the university should be the SSEP of the department as the main division of the university. This system should have multifunctionality and be, on the one hand, a tool for organizing and managing the department for the head of the department, and on the other hand, a means for implementing functional responsibilities for scientific and pedagogical workers. For students and postgraduates, the SSEP should allow achieving educational and scientific goals. In addition, the SSEP of the department should have the ability to store innovative solutions proposed at the department, control the success of the functions of scientific and pedagogical workers, as well as communication with the highest SSEP of the university. Practical value. The implementation of a system to support the educational processes of the department based on integrated intelligence will optimize the work of all department employees and create a basis for improving the quality of higher education.

Electronic course of a discipline as a structural element of the digital educational environment of a university

In recent years, digitalisation has been actively implemented in all spheres of life, and higher education is not an exception. Digital educational environment appears in universities: digital libraries, educational process support systems and distance learning (Diskurs, Flora LMS, Moodle, Docebo, Open edx, etc.), new forms of educational process construction (online courses, video lessons, etc.), various courses to improve digital literacy of university teaching staff, which contributes to the intensification of the learning process. Digitalisation contributes to the activation of independent cognitive activity of the student, provides individually oriented learning. This research deals with the use of digital technologies in teaching mathematical disciplines to students of the Federal State Autonomous Higher Education Institution «South Ural State University (National Research University)», especially for students of Architecture and Construction and Polytechnic Institutes. The use of digital technologies in teaching mathematical disciplines requires teachers’ large time expenditures for the choice of necessary elements of information educational resources and pedagogical technologies. The positive results of digitalisation of education in our study include the possibility of building individual educational trajectories for students, starting from the stage of learning new material to the stage of controlling the formation of competencies; continuous monitoring of educational results of students’ learning activities based on the rating, tracking of gaps with subsequent correction of the learning trajectory. It should be noted that in order to organise the learning process in a digital educational environment, students need to be prepared for self-dependent learning activities.

Using of NLP Methods in Intelligent Educational Systems

For the effective organisation of educational processes supported by relevant intelligent learning systems, it is important to choose the right technologies that would ensure individualisation of learning, adequate perception of learning content, and the so-called “understanding” of texts in Ukrainian provided by students (description of the solution to a task, answers provided in their own words, not selected from the test answer options, questions to the system, etc.), prototyping, constant iteration during natural language text recognition and processing, and maximum reliability and efficiency of learning processes. The purpose of the article is to study and analyse various methods of natural language processing, and the concept of NLP, and to consider common problems and prospects for developing a software product for processing Ukrainian-language text in online courses that support intelligent learning systems based on it. The research methods are the main methodological approaches and technological tools for analysing natural language texts in intelligent educational systems and developing a system for supporting NLP (Natural Language Processing) technology in the linguistic analysis of texts in Ukrainian. Such methods include, in particular: systemic and comparative analyses to identify the features of intelligence and information (with elements of intellectualisation) systems; the method of expert evaluation, which involves the study of literary sources and information resources, interviews and surveys of experts, as well as the processes of developing and testing intelligent and information systems. The novelty of the study is the analysis of modern technologies for the development of online educational process support systems through the organisation of processes of perception of information provided by students in natural language, the results of which can be used in the development of their software product to support the educational process in Ukrainian, ensuring the improvement of learning efficiency through the use of NLP technology in the process of studying the relevant academic content. Conclusions. The paper analyses modern NLP methods. The analysis has led to the selection of tokenisation, normalisation, stemming and lemmatisation methods for use in intelligent learning systems in the linguistic analysis of the so-called “free” communication in the natural (Ukrainian) language of students in the process of studying the educational content of online courses. During the tokenisation of Ukrainian-language texts, we solved such problems as eliminating so-called “merged” tokens, correcting spelling mistakes, identifying common prefixes in compound words and their impact on the semantics of the corresponding lexemes, identifying common prefixes in abbreviations, and bringing words to their normal form. Lemmatisation is especially important for the Ukrainian language (with its large number of cases of nouns, adjectives, word forms, etc.) and it requires the use of specially compiled dictionaries of the subject area under consideration. In these dictionaries, word forms are presented in the forms of lemmas (i.e., nouns are presented in the nominative case).

Developmental Milestone Attainment in US Children Before and During the COVID-19 Pandemic

Restrictions related to the COVID-19 pandemic disrupted the lives of young children, but the association between the pandemic and any changes in early childhood developmental milestone achievement in the US remains unclear. To determine the association between the COVID-19 pandemic and changes in developmental screening scores among US children aged 0 to 5 years and to investigate whether caregivers self-reported more worries about their children or concerns about children's behavior during the pandemic, regardless of milestone achievement. This was a cohort study using an interrupted time series analysis comparing prepandemic (March 1, 2018, to February 29, 2020), interruption (March 1 to May 31, 2020), and intrapandemic (June 1, 2020, to May 30, 2022) periods among 50 205 children (randomly sampled from a population of 502 052 children) aged 0 to 5 years whose parents or caregivers completed developmental screening at pediatric visits at US pediatric primary care practices participating in a web-based clinical process support system. COVID-19 pandemic period. Age-standardized Ages and Stages Questionnaire, Third Edition (ASQ) domain scores (communication, personal-social, problem-solving, gross motor, fine motor), and rate of caregivers' concerns about the child's behavior or worries about the child as measured on the ASQ. A total of 50 205 children (25 852 [51.5%] male; mean [SD] age, 18.6 [16.0] months) and 134 342 ASQ observations were included. In adjusted models, significant age-specific mean score decreases from prepandemic to intrapandemic were observed in communication (-0.029; 95% CI, -0.041 to -0.017), problem-solving (-0.018; 95% CI, -0.030 to -0.006), and personal-social (-0.016; 95% CI, -0.028 to -0.004) domains. There were no changes in fine or gross motor domains prepandemic to intrapandemic. For infants aged 0 to 12 months, similar effect sizes were observed but only for communication (-0.027; 95% CI, -0.044 to -0.011) and problem-solving (-0.018; 95% CI, -0.035 to -0.001). After accounting for age-standardized ASQ scores, caregiver worries about the child increased slightly in the intrapandemic period compared with the prepandemic period (rate ratio, 1.088; 95% CI, 1.036-1.143), but there were no changes in caregiver concerns about the child's behavior. While changes in developmental screening scores were modest (2%-3%), nationwide, this could translate to more than 1500 additional recommended developmental referrals over baseline each month. Modest changes in developmental screening scores are reassuring in the short term but may tax an already overburdened developmental behavioral pediatrics infrastructure. Continued attention to developmental surveillance is critical since the long-term population- and individual-level implications of these changes are unclear.

Developed analytic hierarchy process and multi criteria decision support system for wind farm site selection using GIS: A regional-scale application with environmental responsibility

As determined by empirical analysis, renewable energy sources account for less than 1.73 percent of the developing countries energy market. The western region of Iran, or province of Kermanshah, presents significant opportunities for power exports to neighboring country (Iraq). In this research, an appropriate location for wind farms in Western Iran is determined with the assistance of a combined method of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) description. Electrical, techno-economic, environmental, and geo-infrastructure aspects, as well as their respective sub-criteria, were subjected to a comprehensive analysis and weighting in this study. The Analytical Hierarchy Process (AHP) was utilized to achieve this result. The aforementioned conclusions were reached through an extensive examination of insightful interviews conducted with esteemed experts in the domain of wind energy. Significant suitability for the potential installation of large-scale wind turbines or wind farms is evident in six critical areas, encompassing more than 20 % of the evaluated region, according to the findings of this paper. Conversely, it was deduced that precisely 80 % of the areas examined were deemed inappropriate for such endeavors. The assessment yielded four locations that were deemed suitable, denoted as A, B, C, and D. The southern, western, and central regions of the province contain these locations. The proposed total capacity is 216 MW, which is determined by the combined surface areas of these regions (19 km2, 55 km2, 64 km2, and 40 km2, respectively). An anticipated reduction of 1,262,471 tons in greenhouse gas emissions is anticipated as a result of the implementation and substitution of the proposed wind farms for the gas turbine power plants that presently predominate in Iran's thermal power plant sector. The method used in this article can be extended to all cases of other faculties to measure the potential of wind farm resources.

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

THE ELECTRONIC SYSTEM FOR THE MANAGEMENT OF UNIVERSITY EDUCATIONAL PROGRAMS

The article presents the research results of the electronic system for managing university educational programs (EP). The analysis of scientific works showed that higher educational institutions need the introduction of integrated information systems. Such systems are the technical basis of the electronic university and have functionality for managing education and providing communications to university management. An analysis of the main processes of EP management was performed, and interested parties, their roles, and participation in the methods were determined. The proposed mathematical model is the basis of management for introducing the EP, its development, and implementation control based on quality parameters. The software module is a part of the university's management process support system and the JetIQ of Vinnytsia National Technical University's (VNTU's) comprehensive information system. For the analysis of the subject area and the formation of optimal management processes, the methodological recommendations of the National Agency for Higher Education Quality Assurance (NAQA), data export and import system for the Unified State Electronic Database on Education Issues, the experience, and recommendations of experts from the department of quality assurance of education of VNTU, experts and guarantors of educational programs were used. Precedent models are formed about such role scenarios – applicant, student, teacher, guarantor, management, expert, stakeholder (employer). Such models made it possible to create not only the software module for the management of educational programs (QEd – Quality Educational) but also to determine the conditions for publication on the main website of VNTU of educational programs and documentation of their development, syllabi on the foremost showcase and jet-sites of the department, to create scenarios for automatic quality control of the EP. The result of the creation of the software module for the management of educational programs is a system of structured information regarding the development and implementation of the educational program, which allows the guarantor and specialists of the department to ensure the quality of education by use special containers for documentation and interested parties to use public up-to-date information on the university's websites.

Effectiveness of Clinical Management of COVID-19 Based on Structured Clinical Knowledge and Process Paths.

Between 2017 and 2019, a clinical process support system based on structured clinical knowledge (Team Compass with the Patient Condition Adaptive Path System; TC-PCAPS) was developed, and implemented in hospitals. In 2020, the COVID-19 clinical management system (COVID-19-CMS) was developed. In this study, the effectiveness of implementing both systems was analyzed. The analysis covered hospitals N, T, and B, where TC-PCAPS implementation started in 2019, 2020, and 2022, respectively. Data for the period from 2018 to 2022 were collected and compared. Hospitals N and T implemented TC-PCAPS in the first year and the COVID-19-CMS in the following year. The nurse turnover rates of these hospitals were lower than those of the prefectures in which they were located. There was a trend towards a gradual reduction in nurse turnover. In contrast, hospital B, which had only just started to introduce these systems, saw a gradual increase in nurse turnover. The data collected from these three hospitals suggested that this systematic approach has the potential to reduce nurse turnover, in addition to the previously reported ability of TC-PCAPS to reduce nurse overtime. In Japan, there is a need to respond to future pandemics and reform the work styles of physicians and nurses. The abovementioned systematic approach has great potential for contributing to both of these aims.

Automated educational process support system with dynamic formation of optional components

Automated educational process support system with dynamic formation of optional components

Unveiling groundwater potential zones as catalyst for multidimensional poverty reduction using analytical hierarchical process and geospatial decision support systems (S-DSS) approach in the semiarid region, Jigawa, Nigeria

Integrating agricultural production with the identification and use of groundwater resources has been shown to reduce multidimensional poverty in semi-arid regions. Poverty reduction and socioeconomic growth depend on sustainable water supply, especially in developing countries with limited rainy seasons. Poverty eradication is a top priority among the 17 Sustainable Development Goals (SDGs), and its reduction in the 21st century has led to significant advances in research. This study used remote sensing, geographic information system (GIS), and geospatial decision support system (S-DSS) approaches to uncover potential groundwater zones. The Analytic Hierarchy Process (AHP) integrates geospatial data to create a groundwater potential zone map and suggests the best land requirements for groundwater abstraction for poverty alleviation programs. The groundwater potential zone maps indicate that the majority of the region was in the high-potential zone, covering 59.75% of the total area, followed by a moderate-potential zone at 23.21%, an extremely high-potential zone at 14.6%, a low-potential zone at 2.44%, and an extremely low-potential zone at 0%. In addition, the study emphasizes the need for alternative water sources and infrastructure development in dry seasons in areas with fewer drainage systems and proposes measures such as rainwater harvesting structures and small reservoirs. Diversifying income sources by promoting alternative livelihoods can help reduce poverty and vulnerability to fluctuations in agricultural productivity. The integration of socioeconomic data into the S-DSS framework will provide a comprehensive understanding of the complex relationship between groundwater resources, poverty, and socioeconomic development, enabling informed decision-making in water resource management for poverty reduction initiatives and the achievement of the 2030 Agenda for Sustainable Development Goals.

AI-Based Decision Support System for Traumatic Brain Injury: A Survey.

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

Information support system for household waste disposal processes

In work, a web application was designed and developed to support interaction between members of different households regarding the disposal of household waste and the sale of used goods on the secondary market. To minimize the negative human impact on the environment, the developed information system to support household waste disposal is based on the principles of a circular economy, where all available resources are reusable, goods to recycle, and in general, the level of consumption is reduced. The need to form the average consumer’s interest in finding a target audience for the distribution process on the secondary market of used goods is substantiated. The main distinguishing feature of the web application is providing consumers with an electronic platform that would provide an opportunity to successfully realize the need for sorted garbage (household waste, recyclables, etc.) with economic benefits for all participants of the interaction. As a result of the task, we carried out an analysis of knowledge in the direction of household waste disposal and existing applications to support circular economy processes. According to the compiled requirements specification, we used the TypeScript programming language, the React library, the SASS scripting metalanguage, and the MVC architecture. To ensure the correct operation of the service, manual, modular and automated testing was carried out. The developed software product has a simple interface. It is easy to use for every user who has access to the Internet and a desire to obtain additional resources in the difficult time of Ukraine’s military economy.

FPGA Implementation of Expert System for Medical Diagnosis of Disc Hernia Diagnosis Based on Bayes Theorem

The aim of this research is to create a medical expert system based on Bayes theorem to diagnose level of disc hernia based on real foot force measurement signals obtained using sensors and implement the whole system on field programable gate array (FPGA). We have created a database of attributes based on recorded foot force values of 33 patients pre-diagnosed with herniated disc on levels L4/L5 or L5/S1 on the left or right side. The results obtained by software (Matlab) and hardware (FPGA simulation) are matching well, achieving high accuracy, which shows that VHDL implementation of Naïve Bayes theorem for disc hernia diagnostics is adequate. The output on FPGA is easy to understand for any user, as it is implemented as four-bit output where the position of bit value 1 indicates the level of disc herniation. The system is able to distinguish between the healthy subjects and subjects with disc herniation and is able to detect if improvement in stability is present after surgery or physical therapy. Our proposed measurement platform can be coupled with FPGA to create a portable and not expensive tool for real time signal acquisition, processing and decision support system in disc hernia diagnosis and post-surgical recovery.

중등 예비교사가 인식하는 학교현장실습에 대한 탐색

The purpose of this study is to explore the actual conditions of school field practicum recognized by secondary school preservice teachers and to suggest effective directions. Therefore, this study collected data from pre-service teachers enrolled in the Department of Education at University A located in the metropolitan area. In addition, this study explored the practice process, practice type, and practice support system in the practice school, and also explored the method of effective secondary school field practicum. Suggestions based on the results of this study are as follows. First, even in an emergency such as COVID-19, the period of secondary school field practicum should not be reduced, but should be maintained. Second, the practice school should establish a systematic practice plan that organically connects 'preparatory education-main practice-post evaluation'. Third, universities, which are secondary teacher education institutions, should reinforce their own pre-training. Considering that school field practicum in front-line practice schools is not systematic and takes place in a short period of time, it is necessary to prepare and operate a practice program on its own. In this study, implications for activating school field practicum in secondary teacher training institutions in a desirable direction were presented.

Automatic irrigation control system using Internet of Things(IoT)

The water deficiency for agriculture around India has pushed a need for optimal utilization of it. Internet of Things (IoT) is today’s emerging trend which bridges the gap of real world with the technology. An IoT based automatic irrigation control systems helps in the optimal water utilization for the crop fields. This article proposes an automatic irrigation control system which is based on Internet of Things to predict water requirement of a crop field by processing the wireless sensor network’s (WSN) sensor data like relative humidity, moisture of soil, temperature of soil, and radiation of light (Ultraviolet) along with the Indian Meteorological Department’s weather forecast. Considering the sensed data from the environment with the weather forecasting data, a machine learning algorithm for soil moisture prediction is developed to evaluate the water requirement for a particular area of agricultural land for that crop yield. Real time information of the agriculture landscape is provided through a smart information processing and decision support system which is implemented using schematic web service. This article discusses and describes the system implementation in detail. The results by the automatic irrigation control system based on the soil moisture prediction algorithm are presented. The irrigation control system is completely automated and soil moisture prediction results are nearest to reality.

A regeneration process chain with an integrated decision support system for individual regeneration processes based on a virtual twin

Regeneration processes of complex capital goods such as components of aircraft engines are highly complex and require a multi-disciplinary perspective, bringing together expertise from different fields such as production engineering, image-based measuring technology, turbomachinery, and operations management. Sustainability requirements and digitalisation opportunities both demand and suggest systematic, flexible, and economically efficient design and operation of the regeneration process chains. This multi-disciplinary case study documents the design and operation of a cyber-physical system demonstrator for individual, flexible and economically optimised maintenance, repair, and overhaul (MRO) actions on extremely valuable components of aircraft engines: high-pressure turbine blades. This system demonstrator hence brings together research results from different disciplines considered jointly in the German Collaborative Research Centre 871 ‘Product-Regeneration’. To cope with the substantial variability in used-good damage patterns and the resulting MRO actions, the system demonstrator operates with a virtual layer and uses a virtual twin of the object to be regenerated. In addition to a functional simulation, a central component of that virtual layer is a project-scheduling-based decision support system used to both select the MRO actions and control the shop floor. This paper hence describes how recent research results can be combined to achieve innovative, efficient, and sustainable regeneration processes.

Information Processing and Data Analytics for Decision Making

Decision making is required by all organizations; however, the decision making styles may differ. ost commonly used decision styles include: (a) Autocratic (b) Democratic (c) Consensus and (d) Participatory. With the Globalization and expansion of businesses, professionals have become highly dependent upon the technology to support the decision making process and decision support systems have come-up as a fastest growing discipline. Present work discusses the evolution of computerized decision support, considering the: (a) Model Driven (b) Data Driven (c) Communication Driven (d) Document Driven and (e) Knowledge Driven decision support systems. All three different business levels: Operational, Tactical and Strategic have been considered in the present work to review the development of decision support systems. The traditional data analysis based approaches have been compared with the latest data analytics approaches including the social media analytics and web analytics. Examples from the different industry sectors have been incorporated for better illustrations of decision support.

A decision support system for coagulation and flocculation processes using the adaptive neuro-fuzzy inference system

Decision support system (DSS) is an approach to have a smart and sustainable management of facilities for monitoring, predicting and controlling sections. The mentioned platform can be useful in operation of complex facilities like the water treatment plant (WTP). This study proposes an adaptive neuro-fuzzy inference system (ANFIS) for prediction of energy consumption and outlet turbidity according to inlet turbidity and ferric chloride as coagulant in coagulation and flocculation unit process of WTP. The outcomes of ANFIS model are used in the Petri Net modeling as a smart conceptual control system. Therefore, the main purpose of this research is the development of a DSS model for coagulation and flocculation processes in WTP. The results of quantitative data analysis showed that the correlation coefficients of ANFIS model are more than 80% meaning that it can reliably predict the outlet turbidity and energy consumption’s variables. With regards to our findings, the first one is to provide a smart and sustainable control system to be implemented in operations of coagulation and flocculation processes in WTPs. It goes without saying that, our DSS model confirms that the variation of 15 ± 5% for turbidity values and the additive coagulant materials (ferric chloride) should be set, on 60–85 and 40–60 kg/day, respectively, for controlling energy consumption and outlet turbidity. At last but not least, the main benefit from our DSS model is to manage the operation of WTP with a high efficiency and low human-based errors.

DSS-Based Process Control and FMEA Studies for Different Processes in the Field of Textile

Today, in many textile firms, high defect rates and inefficiency stand out due to the intensive labor in the production processes. For this reason, to be able to detect failure modes and effects, number of defects and defect rates fast and intervene in the process just in time is vital for textile firms. Accelerating feedback by producing accurate and effective quality reports and helping senior management's decision processes will reduce appraisal costs and delivery time. In this study, Failure Mode and Effect Analysis and Statistical Process Control techniques have been integrated with a Decision Support System for different processes of textile. Thanks to the proposed integrated system in the fast fashion/textile sector, defect and/or failures will be prevented or detected on time by determining the sources of error with an effective database and monitoring system. So, process capability will increase by preventing material, operations and human-induced scraps.

Machine learning in orthodontics: Challenges and perspectives.

Artificial intelligence (AI) applications have significantly improved our everyday quality of life. The last decade has witnessed the emergence of up-and-coming applications in the field of dentistry. It is hopeful that AI, especially machine learning (ML), due to its powerful capacity for image processing and decision support systems, will find extensive application in orthodontics in the future. We performed a comprehensive literature review of the latest studies on the application of ML in orthodontic procedures, including diagnosis, decision-making and treatment. Machine learning models have been found to perform similar to or with even higher accuracy than humans in landmark identification, skeletal classification, bone age prediction, and tooth segmentation. Meanwhile, compared to human experts, ML algorithms allow for high agreement and stability in orthodontic decision-making procedures and treatment effect evaluation. However, current research on ML raises important questions regarding its interpretability and dataset sample reliability. Therefore, more collaboration between orthodontic professionals and technicians is urged to achieve a positive symbiosis between AI and the clinic.