International Institute of
AI-Enhanced Multilingual Lexicography for Digital Communication
Makhachashvili, Rusudan; Semenist, Ivan; Klochkov, Vladyslav (Ukraine)
https://doi.org/10.54808/IMCIC2025.01.247
ABSTRACT:
In the early 21st century the human mind has progressed in the methods of reality perception. The inquiry objective is the investigation of the innovative philosophicaspects cyberspace through the lenses of the language developmentprocesses in the sphere of new computer technologies and digital communication. The study design is the disclosure of cyberspace as an ontology model and as a logosphere model. The linguistic philosophy approach to the study of cyberspace allows to efficiently investigate the empirical manifestation of cyberspace ontology (space and time dimensions), the generic categories and dimensions of cyber-epistemology, to denote existential anthropocentric character of cyberspace. Philosophical foundations of the study of cyberspace as an integrated macro-and micro-entity are determined by the substantive features of innovative logosphere as a macrostructure and by the phenomenological characteristics and properties of substrate of linguistic units of innovative cyberspace logosphere. The Cyber-speak is an ongoing electronic, multimodal lexicographic project that is based on the study of the late XX – current XXI century European and Asian languages vocabulary integral dynamics within the emergent digital technology framework.
A methodology and AI-run protocols of computer vocabulary innovative elements phenomenological features identification is introduced supplying the template for a new study field – phenomenological, AI-enhanced digital neology, neography and neosemiotics.
Architecture Design of Scalable Blockchain in IoT Smart Cities
AlRamouni, Suad; AlKassim, Norah (Saudi Arabia)
https://doi.org/10.54808/IMCIC2025.01.48
ABSTRACT:
Internet of Things (IoT) is the key of creating smart environment. It refers to the distributed objects as sensors and devices for exchanging information, due to its data centralization that is the main reason of a one-point failure and lack of privacy; it has been integrated recently with blockchain to raise the memory storage and security. Scalability has been figured as an issue for blockchain in large scale IoT due to the high load processing; therefore, we are proposing an architectural design for a scalable blockchain to be used in large scale IoT. Methodology of proposed design consists of expanding the blockchain by sharding technique where each node in the main chain has a sub chain and each node in sub chain has sub-sub chain that will enhance the process of creating a block on the chain in parallel with less time.
Autonomous AI Agents – The Kraken Wakes
Cowin, Jasmin (United States)
https://doi.org/10.54808/IMCIC2025.01.20
ABSTRACT:
John Wyndham’s The Kraken Wakes offers a prescient allegory for modern anxieties surrounding the unchecked rise of autonomous AI agents. This paper explores the parallels between Wyndham's depiction of societal denial and the contemporary challenges of algorithmic opacity, bias, and unintended consequences in AI systems. Drawing on case studies of discriminatory hiring algorithms, biased criminal justice tools, and facial recognition misidentifications, it examines how poorly regulated AI perpetuates systemic inequalities and exacerbates societal harm. The narrative arc of Wyndham’s xenobathite invasion resonates with the existential risks of unmonitored AI development, highlighting the dangers of marginalizing scientific expertise and underestimating emerging threats. The commodification of personal data and lack of transparency in AI decision-making further underscore the urgency for robust governance and ethical safeguards. By juxtaposing Wyndham’s cautionary tale with real-world examples, this paper underscores the necessity of international collaboration and proactive policies to address the profound challenges posed by autonomous AI agents. As society navigates the complex interplay between technological innovation and accountability, Wyndham’s warning about the perils of inaction and denial serves as a timely and critical call to action to a broad spectrum of stakeholders from policymakers to regulators; from corporate entities to AI developers; from researchers, ethicists to civil society.
Beyond Traditional Metrics: Embedding Sustainability and Stewardship in Innovation Measurement
Bancroft, Justin *; Bancroft, James ** (* Latvia, ** United States)
https://doi.org/10.54808/IMCIC2025.01.12
ABSTRACT:
Essential to understanding economic growth, competitiveness and societal progress is the measurement of technology and innovation. This paper highlights systematic relationships between measurement frameworks and organizational practices and examines how the Oslo Manual shapes standardized approaches to innovation assessment. Pathways for evaluating technological progress are established, through analysis of the structural connections between measurement systems and organizational outcomes. The roles of input (research and development spending, human capital) and output indicators (patents, products), in evaluating innovation efficiency and impact are discussed. At the same time, it looks at technologyspecific measures and their implications for public sector policies and decision making on innovation. Finally, the paper also spotlights challenges in documenting sustainability-driven innovations, intangible outputs from digitalization, and nonmarket contributions. It recommends refining innovation metrics through stewardship principles, sector-specific case studies, and predictive analytics.
Conceptual Model of the Company's Cyber Resilience Elements
Bahmanova, Alona; Lace, Natalja (Latvia)
https://doi.org/10.54808/IMCIC2025.01.172
ABSTRACT:
This study is a continuation of the paper from last year's conference "Cyber Risks: Systematic Literature Analysis". The work considered the process and risks of digitalization. As a result, definitions of cyber risks and cyber threats, cyber security and cyber resilience were given, and differences between similar concepts were considered. Also, as a result of the work, gaps in current approaches to small and medium enterprise (SME) cyber resilience were identified. To eliminate these gaps, it is necessary to see the full picture, understand which elements are included in the company's security system from cyber threats and are also able to strengthen its cyber resilience against threats and how it is possible to minimize cyber risks. In this work, an attempt will be made to build a conceptual model of cyber resilience of small and medium enterprises, considering the elements of the model and their interrelations and interdependencies.
Continuum of Academic Collaboration: Issues of Inconsistent Terminology in Multilingual Context
Lipuma, James *; Leon, Cristo *; Cabobianco, Marcos O. **; Daizo, Maria B. ** (* United States, ** Argentina)
https://doi.org/10.54808/IMCIC2025.01.36
ABSTRACT:
This article investigates the challenges posed by inconsistent terminology in academic collaboration, particularly in multilingual contexts, focusing on Spanish and English. It begins by outlining the evolution of academic disciplines and the increasing need for collaborative research, providing foundational definitions that frame the discussion. The Continuum of Academic Collaboration (CAC) is introduced as a conceptual framework for categorizing different modes of collaboration. Through scholarly presentations and research discussions, the authors identified a significant gap in the translation and interpretation of key technical terms—especially those used to differentiate between multidisciplinary and interdisciplinary collaboration, such as the prefixes Co-, Cross-, and Across-. The findings indicate that inaccurate translations can lead to misinterpretations that undermine the conceptual integrity of these terms, as direct equivalents often fail to capture their nuanced meanings. A systematic literature review revealed limited clarity and a scarcity of research on these issues in Spanish-language publications. To address this gap, the authors interviewed esteemed experts whose insights underscore the need for further investigation into the multilingual translation of academic collaboration concepts, ultimately aiming to enhance transdisciplinary communication (TDC).
Cybernetics and Informatics of Generative AI for Transdisciplinary Communication in Education
Makhachashvili, Rusudan; Semenist, Ivan (Ukraine)
https://doi.org/10.54808/IMCIC2025.01.254
ABSTRACT:
As a product of modern civilization, the digital reality has become an independent format of being. Accordingly, electronic media act not only as a means of transmitting information, but also reveal their own world-creating, meaning-making and, as a consequence, communicative potential. The global digital realm stands as an integral environment, demanding new cognition and perception ways via complex philosophic, cultural, social, linguistic approaches, providing unlimited opportunities for human intellect, communicative development and research.
Transformative shifts in the knowledge economy of the XXI century, Industry 4.0 and Web 4.0 development and elaboration of networked society, emergency digitization of all social communicative spheres due to pandemic measures have imposed pressing revisions onto interdisciplinary and cross-sectorial job market demands of university level education, curriculum design and learning outcomes. As a product of modern civilization, digital reality has become an independent format of being. Accordingly, electronic media act not only as a means of transmitting information but also reveal their own world-creating, meaning-making, and, as a consequence, communicative potential. The global digital realm stands as an integral environment, demanding new cognition and perception ways via complex philosophic, cultural, social, and linguistic approaches, providing unlimited opportunities for human intellect, communicative development, and research.
Transformative shifts in the knowledge economy of the XXI century, Industry 4.0 (AI-powered technologies and production) and corresponding stages of Web technology development (from Web 2.0 – social media interaction, to Web 3.0 – Internet of things, to Web 4.0 – machine learning powered interaction, LLMs, to Web 5.0 – intelligent personal agents, Web 6.0 – cognitive AI), development and elaboration of networked society and new media ecology, emergency digitization due to quarantine measures and the ongoing warfare have imposed pressing revisions onto interdisciplinary and cross-sectorial job market demands.
The context of the erupted military intervention in Ukraine and the ensuing information warfare in various digital ambients (social media, news coverage, digital communications), the specific value is allocated to the enhanced role of digital humanism as a tool of the internationally broadcast strife for freedom and sovereignty.
Digital Competence for e-Governance in Ukraine
Morze, Nataliia *; Makhachashvili, Rusudan *; Zvonar, Viktor **; Ilich, Liudmyla *; Boiko, Mariia * (* Ukraine, ** Poland)
https://doi.org/10.54808/IMCIC2025.01.1
ABSTRACT:
The study tackles the limits of understanding of EU e-governance principles and practices in Ukraine, since strong EU aspirations of the country are challenged by the warfare threatening the nation existence. The struggle of Ukraine against Russian invasion revealed the benefits of previous digitalization efforts in the public sector. However, civil servants and citizens in the country still feel the urgent need of enhancing digital competence. The public sector developed a clear understanding that further reforms must be aligned with EU experiences and expectations, and a proper expertise is called for. Thus, the research objective is to highlight and disseminate EU experience and best practices of the transition to e-governance. The research project e-DEBUT helps promote EU values of transparency, participatory democracy, and inclusiveness through strengthening the digital community in Ukraine. The study aims to develop an innovative curriculum to enhance skills and competencies of civil servants, enrolled in master’s programs, needed for effective rendering of public e-services in war-time, and transferring knowledge of the tech trends and best e-governance practices of EU countries. The project's meaningful results are: a course syllabus, summer schools’ curricula, and a workshop on the facets of development of e-governance in EU countries and in Ukraine; open digital educational resources and analytical materials; a manual for civil servants on the use of e-governance tools under martial law and through post-war reconstruction of Ukraine. The centerpiece of the study is the development of the study module, covering EU lens on concepts of e-governance and digital state, EU technological trends for e-governance, EU best practices in rendering e-governance for business and citizens, as well as the investigation of the adaptation of EU experience in the use of artificial intelligence and smart city infrastructures to the managerial needs of the country at war.
Digital Neuroplasticity: How Prolonged Technology Use Reshapes Neural Pathways Over Time
ElSayary, Areej; Ragab, Jumana K. (United Arab Emirates)
https://doi.org/10.54808/IMCIC2025.01.158
ABSTRACT:
This study explores the impact of ethical artificial intelligence (AI) usage on university students' academic experiences, performance, and knowledge acquisition. Conducted at Zayed University in the UAE, this cross-sectional study assesses student perceptions of ethical AI practices, including fairness, transparency, and responsibility, using a conceptual framework adapted from prior research. Findings reveal that ethical AI practices, such as the responsible use of adaptive learning systems and AI-driven feedback mechanisms, significantly enhance students' academic outcomes while addressing issues like plagiarism and over-reliance on AI. However, challenges such as biases, reliability, and context understanding in AI tools highlight the need for enhanced development and guidelines. By integrating ethical considerations and reflecting on these challenges, this study underscores the importance of balancing AI’s transformative potential with responsible implementation to foster equitable and effective learning environments. Recommendations for educators, students, and developers include promoting ethical AI usage, developing reliable systems, and enhancing awareness of privacy concerns to maximize AI’s benefits in education.
Effect of Data Imbalance in Predicting Student Performance in a Structural Analysis Graduate Attribute-Based Module Using Random Forest Machine Learning
Lugoma, Masikini *; Zimbili, Abel Omphemetse *; Ilunga, Masengo *; Mosia, Ngaka *; Abhishek, Agarwal ** (* South Africa, ** Bhutan)
https://doi.org/10.54808/IMCIC2025.01.85
ABSTRACT:
This study uses Random Forest algorithm to model students’ final year mark in an engineering technology module taught by the University of South Africa. The algorithm uses a supervised learning classification technique to map the different assessment marks and the final mark. Hence, the latter are labelled instances whereas the former constitute the features. Random Forest (RF) has been applied to Structural Analysis 3, which takes into consideration the graduate attribute concept or level of competence as far as assessments are concerned. Firstly, the RF is subjected to imbalanced binary classes, then balanced classes are achieved by Synthetic Minority Oversampling Technique (SMOTE) and class weights adjustments techniques. The results showed that SMOTE brought an improvement in accuracy of 3%. It was also revealed that an increase of 4, 15 and 9% in precision, recall and F1-Score were observed in predicting non-competent students. An increase of 4 and 3% was noticed in the case of the precision and F1-Score respectively in predicting competent students, whereas the recall did not display any change. Despite the RF with SMOTE overperformed standard RF and RF class weights adjustment, all three algorithms were good candidates in the prediction of student performance. RF-SMOTE could be suggested as a guiding instrument when dealing with imbalanced data.
Effective Leadership Approaches in a Multicultural Environment in a Remote Work Model
Erina, Jana; Kovalova, Anastasija (Latvia)
https://doi.org/10.54808/IMCIC2025.01.198
ABSTRACT:
The multicultural team establishment within the remote working model became part of our daily routine within the corporate world. Consistently with the way of working, leadership approaches within the remote working model should be transformed. The research aims to identify suitable leadership approaches for the multicultural team establishment within remote working model. This research inspects the impact of efficient leadership practices on employee inclusion and creating commonalities within the work goal realizations.
This research aims to estimate what leadership approaches impact cooperation, motivation, and productivity within the multicultural corporate environment. Within the research will be used qualitative and quantitative data processing methods, including literature analysis and a survey questionnaire. The methodology involves developing a survey questionnaire distributed to 50 employees from Information Technologies, Operative, and Administrative roles within the multicultural organization in a financial technology company, where participants have been from Latvia, Lithuania, Sweden, The Czech Republic, Finland, Germany, Belgium, France, Italy, Poland, The UK, Iceland, and Luxembourg. The data obtained from the questionnaire reveal significant suggestions for improving leadership within the remote working environment. The analysis identified approaches that could be separated into four groups: communication-related, such as regular check-ins with the direct manager, regular check-ins with the team, knowledge-sharing sessions, and achievements recognition sessions and right for organizing (supporting employee resource groups (ERGs); work-type-related such as flexible working hours and cross-border remote working policies; salary and benefits-related: regular salary audits among all talent groups and additional paid leave options (childcare (nursery schemes), eldercare employee benefits, family and medical leave, caregiving leave, and emergency leave) and career progression-related: targeted recruitment, retention, and progression initiative.
The research results showed that the inclusion of the individual to the group is realized by having transparent, equal, and multiple rights on regular communication with managers and colleagues, recognition of their work, career, and salary progression, and providing support within the different life situation (medical, family leave, etc.) and flexibility on the working type (remote, cross-border).
Enhancing Random Forest for Continuous Data Streams Using Divergence Measures to Select Decision Trees
Santos, Danilo Rodrigues dos; Silva, Diego Furtado (Brazil)
https://doi.org/10.54808/IMCIC2025.01.127
ABSTRACT:
Traditional machine learning algorithms, such as Random Forest, are significantly impacted by the ongoing growth of data streams in non-stationary environments, with challenges in changes in feature importance and concept drift. In response, we introduce KTree, an enhancement to the Random Forest algorithm. The KTree algorithm processes data streams in fixed-size sliding windows and uses divergence measures such as Kullback-Leibler to identify attributes that signal statistically significant distributional changes. This approach ensures that only decision trees trained on stable attributes are selected for predictions, enhancing robustness in dynamic environments. Our experimental evaluations, conducted on benchmark datasets that reproduce data streams, demonstrate the effectiveness of KTree. The findings reveal that KTree significantly reduces the number of decision trees used in the voting process while still achieving a classification accuracy on par with Random Forest. This reduction minimizes computational overhead, potentially relieves resource usage, and enhances model interpretability without compromising performance. The proposed method demonstrates the potential to improve the efficiency and adaptability of ensemble-based classifiers in evolving data environments. By integrating dynamic tree selection with divergence measures, KTree offers a promising solution for applications in different domains, such as financial, IoT, real-time, and healthcare applications.
Expert System for the Start of Research in Electronic Engineering in a Public University in Peru
Quispe Rojas, Julio Ernesto; Troncoso Castro, Paul Fernando; Quispe Tuesta, Julio Enrique (Peru)
https://doi.org/10.54808/IMCIC2025.01.288
ABSTRACT:
The present study shows the application of the action research methodology in an interdisciplinary project through the use of artificial intelligence technologies to support the initiation of quality research and its conclusion in an adequate time to obtain the professional degree in the public universities of Peru, because it was verified that each year, only the equivalent of 67% of graduates managed to obtain the professional degree, and that the delay in completing the research was on average 3.7 years. The experience of designing and developing an expert system was selected to facilitate the beginning of research in electronic engineering, with the participation in the interdisciplinary project of expert knowledge engineers in artificial intelligence, of teachers related to the thesis as research experts, and student and graduate users as experts in electronic engineering. The expert system is validated in its operation using a set of test data, processed by the expert system and evaluated by human research experts, then compared using a statistical process. The students of the undergraduate Thesis II course validated the usefulness of the expert system, 64% consider it Very Useful and between Very Useful, Useful and somewhat Useful there is 100%.
Increasing the Efficiency of Human Resources Management in Financial Institutions in Latvia
Erina, Jana; Alekse, Aivija (Latvia)
https://doi.org/10.54808/IMCIC2025.01.191
ABSTRACT:
This research examines the efficiency of human resource management in financial institutions in Latvia. Effective human resource management is a critical factor influencing organizational success, regulatory compliance, and workforce engagement, particularly in the financial sector. This research assesses how various human resource management practices contribute to institutional efficiency and competitiveness. The research identifies key efficiency indicators using qualitative and quantitative research methods, including literature analysis and expert evaluations.
The methodology includes a bibliometric analysis of human resource management effectiveness using the Scopus database and VOSviewer visualization tool. Additionally, a focus group discussion with HR professionals from financial institutions in Latvia was conducted to prioritize key efficiency indicators. The Analytical Hierarchy Process (AHP) method and statistical data analysis techniques such as frequency analysis, descriptive statistics, and factor analysis were applied to evaluate the significance of these indicators.
The research findings highlight the most critical factors influencing human resource management efficiency, including compliance with regulations, employee engagement, customer satisfaction, and the availability of digital HR management tools. The study emphasizes the importance of structured evaluation systems in improving workforce performance and institutional success in Latvia’s financial sector.
Interlinguistic Communication: A Topos of Inter-Disciplinary Research in Higher Education and Multinational Companies in a Trans-Disciplinary Way
Nikolarea, Ekaterini (Greece)
https://doi.org/10.54808/IMCIC2025.01.275
ABSTRACT:
The first part of the study will start with what Communication means according to Communication Theory and its various models. Within the context of “communication”, the author will try to define what Interlinguistic Communication means, by examining: (1) who are involved in (sender - receiver), (2) in which language the message is transmitted (encoded) by the sender and in which language it reaches the sender (i.e. how the sender decodes the message), and (3) who and/or what is involved in the transmission of a message when the sender and the receiver speak a different language, that is, the channel of communication [interpreter/translator and/or AI]. As the writer will claim, when a mediator is used in interlinguistic communication the communication can be considered double removed.
The second part of the study will try to describe the three kinds of interlinguistic communication. First, it will present what is involved in interlinguistic communication when a lingua franca (a language of international communication, e.g. English, Spanish) is used for transference of scientific knowledge between global and local scientific environments. This transference of knowledge in a globalized world usually relates to bilingualism/multilingualism, where the sender and receiver communicate with lingua franca, although they come from different linguistic environments. At this point, the author will discuss how human brain and mind (nous, in Greek) functions in bilingual/multilingual environments (e.g. in international Conferences like IIIs). Second, she will present what is involved in interlinguistic communication when a mediator (i.e. interpreter/translator and/or AI) is used despite the fact that this communication may be double removed. Third, she will present the least acknowledged kind of interlinguistic communication, that is, when the sender and the receiver in bilingual/multilingual environments speak different languages, but they are able to communicate with each other without the help of any mediator.
The third part of the study will explore how the above-mentioned three kinds of interlinguistic communication effect (non-English) Higher Education and Multinational Companies, when local students, academics and/or professionals should move to different country and are challenged to perform in a language other than their own mother tongue. As far as (non-English) Higher Education is concerned, this part of the study will focus on the challenges local students, academics and researchers face (e.g. the use of appropriate academic discourse so to be understood by a wider public) when they want to participate in an Erasmus program or in an international conference where they should use a lingua franca, such as English. Concerning Multinational Companies, the following three variables will be discussed in depth: (1) one’s mobility; (2) the duration of one’s staying in a foreign / “unfamiliar” / host country (-ies); and (3) the language(s) one uses to communicate with people of different linguistic and professional environments and cultural origins, i.e., the lingua franca.
This study is inter-disciplinary, since it becomes a topos [a place] where a variety of disciplines, such as: Translation Studies, Psycholinguistics and Neuroscience, Anthropological Linguistics and Sociolinguistics meet. Nevertheless, the author will use a trans-disciplinary way of discussion so that the different complex situations of interlinguistic communication can be understood by people who are not familiar with this kind of communication.
Interoperable Digital Skills for Foreign Languages Education in the COVID-19 Paradigm
Makhachashvili, Rusudan; Semenist, Ivan; Vorotnykova, Iryna (Ukraine)
https://doi.org/10.54808/IMCIC2025.01.262
ABSTRACT:
Transformative potential of the knowledge economy of the XXI century, establishment of networked society, emergency digitization due to the pandemic and wartime measures have imposed elaborate interdisciplinary and interoperable demands on the marketability of Liberal Arts skills and competences, upon entering the workforce. The study results disclose the comprehensive review of dynamics of the digital skills development and application to construe interdisciplinary, AI-interoperable competencies of students and educators in Ukraine through the span of educational activities in the time-frame of COVID-19 emergency digitization measures of 2020-2021 and wartime emergency digitization measures of 2022-2024 in Ukraine (including AI-enhanced communication as a staple of transdisciplinary education as of 2023). The study introduces a model of AI-interoperable digital skills for education and professional application in different social spheres. The survey analysis is used to evaluate the dimensions of interdisciplinarity, informed by the interoperability of soft skills, professional communication skills, and digital across contrasting frameworks of e-competence, professional digital communication and pre/in-service teacher training.
Machine Learning Models for Journalists' Job Satisfaction Using Synthetic Data
Chang, Li-Jing Arthur (United States)
https://doi.org/10.54808/IMCIC2025.01.227
ABSTRACT:
The study used a synthetic dataset from 10,000 rows to conduct a machine-learning analysis of journalists’ job satisfaction. Using synthetic data for social science survey research has a few advantages. This approach overcomes the issue of data shortage due to a lack of accessibility to participants. In addition, synthetic data is generated to replicate real data’s statistical patterns while avoiding the inclusion of individual personal information. This significantly reduces privacy risks, as the data does not directly contain individually identifiable information, enhancing anonymity. The synthesized data can prevent missing values, which often occur with incomplete responses. Moreover, using synthesized data will lower research expenses by avoiding costly human data collection. Lastly, the availability of a large amount of data through synthesization can enhance machine learning model building.
Given the advantages of synthetic data, the study generates a synthetic dataset about journalists’ job satisfaction (including the satisfaction target variable and its features) from a correlation matrix of the key variables obtained from a meta-analysis of 16 samples based on 14 studies. To minimize potential bias of the generated synthetic data, the study uses the means, standard deviations, minimums, and maximums of all the variables (the target and feature variables) obtained from a national journalist job satisfaction study to regulate the generation of the synthetic dataset. As a result, the generated synthetic dataset has a correlation matrix that approximately matches the meta-analysis correlation matrix, and the synthetic dataset has variables with means, standard deviations, minimums, and maximums approximating the pre-imposed constraints. Later, the generated synthetic dataset went through machine learning model-building processes involving seven algorithms to compare the models’ performances in terms of the R2 (R-squared) scores. Among the models built with the algorithms, Ridge Regression and Elastic Net have the highest R2 of 0.565, followed by Lasso Regression (0.564), Support Vector Regression (0.561), Gradient Boosting Regression (0.560), Random Forest Regression (0.547), and K-Nearest Neighbors Regression (0.507).
Modelling Student Performance in a Structural Steel Graduate-Based Module: A Comparative Analysis Between K-Nearest Neighbor and Dummy Classifiers
Ilunga, Masengo *; Zimbili, Omphemetse *; Mampilo, Phahlani *; Abhishek, Agarwal ** (* South Africa, ** Bhutan)
https://doi.org/10.54808/IMCIC2025.01.239
ABSTRACT:
The predictive strength of the K-Nearest Neighbor (K-NN) and Dummy machine learning classification algorithms is investigated for students’ final score. The dependent variable (label) is defined by a binary class, while the different assessments define the independent variables (features). The latter are the module student assessment marks, and the former covers students’ final score. The two algorithms have been applied to the Structural Analysis, which is an engineering technology module taught at the University of South Africa. Competency level or graduate attribute characterises such a module. The results showed that the accuracy values of K-Nearest Neighbor (K-NN) and Dummy algorithms were 0.95 and 0.79 respectively. However, the values of recall, precision, f1-score, support, kappa coefficient and Matthews correlation coefficient, showed that the Dummy model predicted very poorly the “fail” instances, as opposed to the “pass” instances. Thus, the K-NN classifier outperformed the Dummy classifier. The two algorithms could be simultaneously recommended as guiding tools for academics in predicting students’ final score (as fail or pass). However, K-NN is the only algorithm that could be used for both fail and pass.
Navigating Psychological Riptides: How Seafarers Cope and Seek Help for Mental Health Needs
Abadicio, Coleen; Arenas, Stella Louise; Hahn, Rosette Renee; Maleriado, Angel Berry; Mariano, Ramon Miguel; Zabella, Rodolfo Antonio Ma.; Adarlo, Genejane (Philippines)
https://doi.org/10.54808/IMCIC2025.01.150
ABSTRACT:
This study explored the coping strategies and help-seeking behaviors of Filipino seafarers in addressing their mental health challenges, with a focus on the adoption of telemental health services. Drawing on the Integrated Behavioral Model of Mental Health Help-Seeking (IBM-HS), the findings reveal structural barriers, such as financial constraints, digital security concerns, and limited infrastructure, along with cultural influences, significantly shape seafarers’ engagement with mental health care. Telemental health services have been found to offer practical benefits, including convenience, privacy, and timely access to care, particularly in addressing the challenges inherent in maritime work. However, stigma, limited awareness of digital mental health platforms, and distrust in online interactions have impeded their widespread acceptance. The study emphasizes the importance of fostering a supportive culture, enhancing digital literacy, and addressing structural barriers through subsidized services and improved internet connectivity. These insights inform policies for improving mental health care access in the maritime industry, paving the way for interventions that enhance the productivity, well-being, and resilience of seafarers.
Peat Resource Management and Climate Change Mitigation Issues – Case of Latvia
Titova, Anita; Lace, Natalja (Latvia)
https://doi.org/10.54808/IMCIC2025.01.221
ABSTRACT:
Untouched peatlands serve two important functions: they are significant carbon reservoirs and biodiversity hotspots. Despite their ecological importance, the commercial use of peat is essential for many countries, including Latvia. This study aims to 1) identify the factors that influence the effective and environmentally friendly utilization of peat resources and 2) assess how these factors are integrated into national policy frameworks in Latvia.
Using a mixed-methods approach, the research begins with a qualitative content analysis of scientific literature, identifying nine key factors that impact peatland utilization: climate impact, legislation, incentives, dependency on peatland use, infrastructure, land ownership, local traditions, education, and research. Following this, an analysis of Latvian policy documents reveals a commitment to sustainable peatland management, including a phased ban on peat as an energy source by 2030. However, the findings indicate that while the factors are recognized, the mechanisms for optimizing their influence are partly unclear. This study highlights the need for further research to improve the effectiveness of peatland and peat utilization, especially at the firm, excavating and processing peat level.
Platform for Detection of Depression in Medical Students with Improvement Monitoring Using Dialogflow
Abad, Alessandra; Farfan, Gonzalo; Subauste, Daniel (Peru)
https://doi.org/10.54808/IMCIC2025.01.141
ABSTRACT:
Depression is a problem frequently found in university students, and even more common in medical students. According to the study ''Prevalence of Depression, Depressive Symptoms, and Suicidal Ideation Among Medical Students'' carried out globally, 27 of medical students have depression. This untreated disorder can have disastrous results in life. of these students, either for being undiagnosed, or for not going to professionals due to time or money issues. Universities, as professional training centers, must ensure the well-being of their students, and provide them with the necessary support against any obstacle that may arise. Thus, we have proposed the Sequitur platform, made up of a mobile and web application, where universities will be able to subscribe and provide interactive help to all their students, through a mobile application with a chatbot which by Using Machine Learning and Data Analytics, it will detect the percentage of depression they may have, and will put them in contact with their respective psychological area, in addition to providing recommendations and facilitating self-monitoring. This paper details the process that was carried out to create this platform, and the respective validations with experts.
Predicting Land Surface Temperature from Rainfall and Elevation Remotely Sensed Data Using Random Forest Machine Learning in Google Earth Engine
Rogalski, Sebastian; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/IMCIC2025.01.119
ABSTRACT:
Accurate prediction of Land Surface Temperature is essential for environmental management, urban planning, and climate change mitigation and adaptation. The built-in Random Forest model in Google Earth Engine, a cloud-based platform, was used to map the relationship between remotely sensed data, such as rainfall and elevation, and land surface temperature (LST). A case study from the Western Cape Province in South Africa was used. The results showed that the model's accuracy in predicting LST was higher when both data were considered compared to using rainfall or elevation data alone.
Preliminary Assessment of Land Use Land Cover Using Random Forest Machine Learning in Google Earth Engine: Case of a Metropolitan Municipality
Gama, Tinyiko Millicent; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/IMCIC2025.01.76
ABSTRACT:
Machine learning in the Google Earth Engine, which is a cloud-based tool platform, was used preliminarily to classify land use land cover (LULC) in the City of Ekurhuleni Metropolitan Municipality, in South Africa. Precisely random forest (RF) algorithm was applied to derive the 2024 map of the city for 5 main classes: water, urban, forest, business, and barren. The results revealed that the classified areas are 10.4%, 36.6%, 7.2%, 37.2%, and 8.5% respectively. The overall accuracy of the classification map was acceptable, and the map generated was validated against the satellite street open map. The computed municipality classified area was found to be congruent with the value declared in the 2023 Budget Speech of the city.
Preliminary Drought Monitoring Using Normalized Difference Drought Indices in Google Earth Engine Intelligent Tool: Case of Tabuk Area, Saudi Arabia
Dweba, Vukile Allison; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/IMCIC2025.01.70
ABSTRACT:
This study examines various atmospheric data and land use land cover by employing the Normalized Difference Drought Index (NDDI) to categorize drought severity in South Arabia, specifically the Tabuk area. Google Earth Engine (GEE) intelligent tool was used to derive preliminary drought severity categorization for the years 2015, 2020 and 2023 based on Landsat 8 images. Generally, the findings revealed extremely low severe drought, for these years.
Preliminary Flood Mapping Using Cloud Computing: Case of Port St Johns Local Municipality, South Africa
Dhliwayo, Ransom; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/IMCIC2025.01.64
ABSTRACT:
This study harnesses the power of cloud computing, specifically Google Earth Engine (GEE) and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to develop flood maps tailored to the unique needs of Port St. Johns Local Municipality. Using SAR imagery, this research identifies critical flood-prone areas and assesses the influence of topographic and meteorological factors, including elevation and rainfall intensity for the year 2023, preliminarily. The study achieves near-real-time flood detection and comprehensive risk mapping, reducing processing time from days to hours. Findings reveal a direct relationship between high-intensity rainfall, terrain characteristics, and flood distribution, pinpointing specific zones at heightened risk of flood-induced damage.
Prototyping the Future: Educational Competitions for XR and Physical Design
Cwiakala, Martin (United States)
https://doi.org/10.54808/IMCIC2025.01.232
ABSTRACT:
This paper explores the integration of physical prototyping and extended reality (XR) technologies to create immersive engineering experiences for middle and high school students. Inspired by a VR/AR/XR course by Michael Nebeling at the University of Michigan, the study revisits an earlier project where students mounted an analog camera on a K’Nex Ferris Wheel to simulate a ride experience. Engineering challenges included counterbalancing and structural reinforcement for camera and battery weight. First-person view (FPV) technology advancements, including lightweight cameras and head-mounted displays, have reduced costs and expanded accessibility, enabling more engaging hands-on projects. This paper proposes a middle school engineering competition in which students design, build, and document working dioramas of amusement park rides. Modular design and transportation strategies mirror real-world challenges faced by traveling amusement rides. The competition extends to high school and college levels, incorporating XR applications such as drone-based augmented reality (AR) simulations. Students develop mechanical design, structural analysis, and project documentation skills while fostering teamwork and creativity. Community outreach opportunities include school fairs, children’s hospitals, and state fairs, connecting engineering education with real-world applications and impact.
Quantitative Endosurgery Process Analysis by Machine Learning Method
Nokovic, Bojan; Lambe, Andrew (Canada)
https://doi.org/10.54808/IMCIC2025.01.112
ABSTRACT:
We show how to analyze endoscopic surgical data using a supervised machine learning (ML) classifier. Before the process starts, a computer-generated 3D image representing a safe zone is inserted into the endoscopic view. During surgery, the LaparoGuard Augmented Reality System collects positional data. We perform two types of analysis on the collected data. First, we analyze how the surgeon handles laparoscopic surgical tools from the angular velocity and the angular acceleration of the tool. Next, we examine the risk of the entire surgical process concerning the safe zone from all collected data, including the average linear speed and the average angular speed of the surgical tool.
Railway Track Degradation Modelling Using Finite Element Analysis: A Case Study in South Africa
Lunga, Ntombela; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/IMCIC2025.01.93
ABSTRACT:
This study utilizes finite element analysis (FEA) to examine railway track degradation, emphasizing the performance and durability of rail components under varying loading conditions. Static, dynamic, and fatigue simulations are conducted to identify high-stress regions, particularly at rail joints and sleeper interfaces, which are most prone to accelerated wear and failure. A refined three-dimensional (3D) modelling approach enhances simulation accuracy, providing detailed insights into stress distribution and contact dynamics. NX Nastran is used for static and dynamic load analyses, while SolidWorks is used for fatigue evaluations to predict material resilience and failure points under cyclic loading. These findings support the development of effective maintenance and reinforcement strategies, contributing to enhanced track longevity and safety.
Real-Time Performance and Accuracy in Anomaly Detection by a Hierarchy of Crowdworkers
Tamano, Tatsuki; Itano, Ryuya; Tanitsu, Honoka; Koita, Takahiro (Japan)
https://doi.org/10.54808/IMCIC2025.01.137
ABSTRACT:
For anomaly detection, many proposed systems have used dedicated models and crowdsourcing. Crowdsourcing systems recruit anonymous workers on the Internet to accomplish specific tasks. Anomaly detection by crowdsourcing is achieved using the responses of multiple crowdworkers, but the accuracy of this approach is low. One possible cause is the influence of spam workers, who accomplish tasks in an inappropriate manner to earn a large amount of compensation. To eliminate spam workers, previous work has proposed a filtering method using qualification tests. In this method, workers are required to perform a qualification test before working on a task, and only those workers who meet passing criteria are allowed to work on the task. Unfortunately, such a filtering method can significantly reduce real-time performance. In this paper, we propose a method to improve real-time performance by introducing a partial filtering method through the hierarchization of crowdworkers. Experimental results show improved real-time performance while maintaining nearly the same level of accuracy.
Risks in Managing Smart Factories
Nikolov, Borislav; Koleva, Nataliya (Bulgaria)
https://doi.org/10.54808/IMCIC2025.01.207
ABSTRACT:
In recent years, the pace of technological development has been extremely intense. This undoubtedly brings changes to people's way of life and the way business processes are designed and managed in enterprises across all industrial sectors. The digital transformation of business models, driven by the Industry 4.0 concept and the creation of so-called Smart Factories, has significantly altered the operational functioning and process management within organizations. These changes necessitate the transformation and adaptation of all system elements to maintain a sustainable balance and ensure effective operation. In this transformation process, risk management becomes critically important for ensuring the reliability, security, efficiency, and sustainability of the system/enterprise.
This study focuses on examining the multilayered challenges and risks associated with the functioning and management of intelligent manufacturing systems, including technological deficiencies, information vulnerabilities, risks related to the human factor and human-machine interaction, as well as those arising from environmental impacts. In connection with this, an empirical study has been conducted, leading to a summarized classification of risk groups related to the functioning, management, and development of the technological infrastructure of smart enterprises. This classification can serve as the foundation for a conceptual framework for effective risk management, aiming to enhance the resilience and efficiency of intelligent manufacturing systems.
Strategic Selection of Application Area for Optimizing Computational Complexity in Explainable Decision Support System Using Multi-Criteria Decision Analysis (MCDA)
Ezeji, Ijeoma Noella; Adigun, Matthew Olusegun; Oki, Olukayode (South Africa)
https://doi.org/10.54808/IMCIC2025.01.24
ABSTRACT:
Explainable Decision Support Systems (XDSS) have emerged as a critical tool for integrating artificial intelligence (AI) into decision-making processes, combining predictive accuracy with interpretability to foster user trust and accountability. Despite their increasing adoption across various domains, XDSS face significant computational challenges, including data complexity, scalability, real-time processing demands, and ensuring fairness and robustness. These challenges are further compounded by the unique requirements and constraints of different application areas, which directly influence system performance and utility, making the strategic selection of application areas a crucial step in optimizing XDSS performance. Therefore, this paper employs an adaptation of Multi-Criteria Decision Analysis (MCDA) to systematically evaluate and rank potential application areas based on domain-specific factors such as data characteristics, explanation requirements, and computational constraints. Through a detailed analysis of challenges and application contexts, this paper underscores the importance of domain selection in maximizing the practical utility and computational efficiency of XDSS. The findings emphasize that selecting the right application area is foundational to ensuring XDSS efficiency and highlight how the MCDA framework can be extended to support further configuration decisions within selected domains. This paper contributes to the strategic planning and development of future XDSS frameworks, offering guidance for developers and business leaders aiming to implement these systems more effectively.
Tax Benefits of the Income Tax Systems of the Baltic States: Comparison and Assessment
Sproge, Ilze; Lace, Natalja; Joppe, Aina (Latvia)
https://doi.org/10.54808/IMCIC2025.01.216
ABSTRACT:
The tax systems of the Baltic States—Latvia, Lithuania, and Estonia—each offer distinctive approaches to personal income tax (PIT) relief, balancing economic and social priorities in different ways. Latvia and Lithuania utilize progressive tax systems aimed at reducing income inequality through targeted allowances, while Estonia's flat tax structure emphasizes simplicity and economic efficiency. This article compares and evaluates the PIT relief systems in these countries, examining their theoretical foundations, practical applications, and socioeconomic impacts from 2000 to 2024. The analysis employs theoretical frameworks, case studies, microsimulation models, and cost-benefit and statistical evaluations. The findings reveal that while progressive systems effectively mitigate inequality, they introduce administrative complexity. In contrast, Estonia’s flat-rate system encourages investment and economic activity but falls short of addressing redistributive goals. This comparative analysis highlights the trade-offs inherent in PIT systems and offers insights into optimizing tax policies for balanced economic growth and social equity.
The Impact of Artificial Intelligence on the Competencies of School Career Counselors from the Perspective of Sustainable Development
Burceva, Rita; Oganisjana, Karine; Dzerviniks, Janis (Latvia)
https://doi.org/10.54808/IMCIC2025.01.184
ABSTRACT:
This research examines the professional competence of school career counselors in the era of AI, with a particular emphasis on sustainable development. Extensive research conducted in various countries reveals a significant rate of higher education students who discontinue their studies, often realizing that their chosen profession does not align with their interests or needs. This results in wasted time, human effort, and financial resources—an unsustainable outcome on both individual and global levels. Such findings underscore the vital role school career counselors play in guiding students toward well-informed decisions about their career paths. Based on a qualitative content analysis of relevant scientific literature and documents, this study identifies 13 key categories that define the professional competence of school career counselors and highlights AI tools that can enhance the effectiveness of their work. The paper also details an experiment conducted by the authors, concluding that while AI cannot replace school career counselors, it can serve as a valuable partner. Together, counselors and AI can form a powerful combination to support students in realizing their full potential.
The Impact of Globalization on the non-English Higher Education: Case Studies of (Mixed/Blended) Referencing
Nikolarea, Ekaterini (Greece)
https://doi.org/10.54808/IMCIC2025.01.7
ABSTRACT:
This paper is a treatise on the impact of globalization on referencing sources from languages that do not use the Latin alphabet, such as Greek, Russian, Ukrainian (and any other Slavic Languages that use the Cyrillic alphabet), Chinese, Japanese as well as from languages that are written from the right side of the page to the left, such as Arabic, Hebrew, Urdu, Hindi. This is a tantalizing issue for international (non-English) scholars when they want to communicate their own research and cite valuable research done in their own countries whose language is other from English, especially a language that does not use the Latin alphabet. The paper starts with the notion of linguistic glocalization, that is, when the global (English, as lingua franca, a means of international communication) comes in contact and interacts with the local (languages other than English – and - within the present context – languages that do not use the Latin alphabet). Then, it provides a couple of examples of how Greek references (local) have been cited in international journals, using either English (global) or simply transliterated Greek into English. Finally, it tries to systematize how referencing in languages that do not use the Latin alphabet can be done (providing specific examples), by using AI (in the form of Microsoft Word, jpg, and the Internet) and discussing how useful Informatics and AI are for this systematization.
Transformation of Residential Property Price in Georgia Against the Backdrop of Global Events
Abesadze, Nino; Abesadze, Otar; Kinkladze, Rusudan; Paresashvili, Nino; Robitashvili, Natalia; Chitaladze, Ketevan (Georgia)
https://doi.org/10.54808/IMCIC2025.01.165
ABSTRACT:
The residential real estate market in Georgia has experienced significant growth in recent years, driven by various changes and trends.
The article discusses the main trends in the price of residential real estate against the background of the current changes in the market of residential apartments in Georgia. In general, the determining factors of apartment prices are analyzed and an analysis of price changes according to prestigious districts and places in Georgia is given.
Accordingly, the objective of this research was to conduct a statistical analysis of price fluctuations in the residential real estate sector in Georgia.
Results: The real estate market in Georgia is attractive to both local and international investors. Investment purchases account for 32% of the demand in the market. On average, rental prices in Tbilisi have doubled. The number of building permits has been steadily increasing, and there is no significant imbalance between supply and demand. Property prices for both apartments and private houses are on the rise, with Didi Digomi and Saburtalo leading in terms of real estate sales in Tbilisi.
The average price of real estate in Tbilisi is $1,233 per square meter, representing a 42.6% increase compared to January 2022 and a 100% increase since 2020.
Several factors have contributed to this growth, including the easing of the National Bank's monetary policy, greater accessibility to mortgage loans, heightened foreign investor activity, and a rising interest in private homes. These factors have collectively led to an increase in the number of people looking to purchase properties.
Transformative, AI-Enhanced, Transdisciplinary Digital Educational Communication for Resilience
Makhachashvili, Rusudan; Semenist, Ivan (Ukraine)
https://doi.org/10.54808/IMCIC2025.01.267
ABSTRACT:
The emergency (warfare-induced) and sustainable (pandemicinduced) digitization changes in the higher education sphere of Ukraine heralded the enhancement of pervasive dimensions of learning – digital, hybrid, and blended, synchronous and asynchronous, AI-enhanced.
This end-to end digital shift in the educational processes (communication, content, outcomes and outputs, skills) introduced the meta-disciplinary dimensions of learning, mitigated by human-in-the-loop and AI-in-the-loop formats of educational communication. These meta-disciplinary dimensions can be considered conduits of vertical (endocentric) and horizontal (exocentric) transdisciplinary of digital education as a sustainable dynamic system.
Applied trans-disciplinary lens contributes to the solution of holistic modeling of processes and results of updating models and mechanisms of the highly dynamic communication system of education in the digital environment as a whole and its individual formats in the emergency digitization measures of different types to foster resilience and sustainability.
Trend Analysis of Remotely Sensed Historical Rainfall in the Olifants River Watershed, South Africa
Nkoabela, Matsela Tlhologelo; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/IMCIC2025.01.105
ABSTRACT:
This study investigates the presence of climate change in the temporal variations of historical rainfall by utilising satellite-based precipitation data from 1993 to 2023 for the Olifants River watershed. Data were derived from the Center for Hydrometeorology and Remote Sensing (CHRS) Rainsphere system. The historical annual mean rainfall was found to be 621.45 mm, with the highest and lowest values observed in the year 2000 and 2015, respectively. A notable declining trend in annual rainfall was identified, while seasonal data showed a declining rainfall trend in Spring. It was also revealed that the spatial rainfall distribution from CHRS Rainsphere had little in common with the one derived from the observed rainfall data provided by the South African Weather Service.
Using Geospatial Computation Intelligence for Mapping Temporal Evolution of Urban Built-up in Selected Areas of the Ekurhuleni Municipality, South Africa
Correia, Jo-Anne; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/IMCIC2025.01.54
ABSTRACT:
Rapid urbanization in Ekurhuleni Metropolitan Municipality has transformed local landscapes, impacting stormwater management, rainfall, and temperature patterns. This study introduces a novel approach by integrating Google Earth Engine (GEE) with Geographical Information Systems (GIS) to analyze urban growth trends over multiple decades, highlighting both the spatial extent and the rate of expansion. Using geospatial analysis of remotely sensed data, this preliminary study mapped urban growth from 1990 to 2030, in the suburbs of Alberton, Boksburg, Brakpan and Kempton Park. The results demonstrated how readily available maps can be used to depict the rapid urban growth in the area of interest. Additionally, this study found substantial increases in impervious surfaces, which suggests increased runoff and reduced water infiltration into the soil, with adverse consequences on stormwater systems. Furthermore, such maps could be useful for urban planning and sustainable water resource management, with particular attention to monitoring built-up areas.
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