Analytic Tools Research Articles

Data-Driven Teamwork

Group assignments are a staple in higher education, providing opportunities to enhance collaboration and communication skills. However, we frequently encounter grievances related to group work as coordinators of large STEM units (800 students). Our student survey identifies work ethic and communication discrepancies between group members as barriers, often leading to uneven workloads, dissatisfaction, and diminished collaborative learning (Bacon et al., 1999). Common mitigation strategies to address work ethic discrepancies, like group charters and peer evaluations, often prove ineffective, since they could be ignored if not properly followed up or enforced (Chang & Brickman, 2018). Another barrier to group productivity lies in the team formation process itself. Traditional team formation methods – self-selection, random assignment, or instructor-led grouping – each present inherent limitation. Self-selection often results in homogenous groups susceptible to "groupthink" (Janis, 1982), limiting the diversity of thought essential for creative problem-solving. Random assignment, while seemingly impartial, can lead to imbalanced skillsets and an uneven distribution of workload (Bacon et al., 1999). While instructor-led grouping might seem ideal, it is logistically impractical within large cohorts (Decker, 1995). Computer-aided team selection offers a promising alternative. Its automated process streamlines team formation, generates diverse teams, and allows instructors to focus on teaching, despite concerns about increased workload (Cavanaugh et al., 2004). To address these group work challenges, we have developed a multi-pronged strategy integrating computer-aided team selection with a comprehensive follow-up and feedback framework. Our goal is to transform group work from a source of anxiety to a rewarding learning experience. Our approach combines technology with strategic interventions: Computer-aided Team Formation: We utilise Gruepr, an open-source team selection software (Hertz, 2021), to generate groups based on student preferences including work styles and meeting preferences (Cavanaugh et al., 2004). Gruepr integrates seamlessly with our learning management system, Canvas. Structured Communication, Collaboration, and Proactive Monitoring: Regular temperature-check surveys deployed through Canvas allow students to rate their group's progress and workload distribution. This data enables early identification of potential issues. To encourage group dialogue, the aggregated and anonymised outcome is shared with team members Facilitating Peer Feedback and Communication through SRES: The Student Relationship Engagement System (SRES), a learning analytics tool (Liu et al., 2017), was used to communicate aggregated peer feedback to individual group members, encouraging constructive dialogues regarding group progress. Our evaluation compared student performance, feedback, and complaints from the current year to previous years, alongside an estimation of staff workload. Preliminary findings demonstrate a reduction in staff time spent on group formation using Gruepr's automated grouping, highly contingent upon accurate survey design. Complaints, though still reported, were less severe, suggesting that regular temperature checks promoted proactive communication. Importantly, student feedback provided valuable insight, with many groups identifying the need for increased in-person interaction and improved scheduling strategies to accommodate diverse commitments. Our poster will elaborate on these results, showcasing both successes and challenges, and propose a refined workflow to further enhance collaborative learning experiences.

Data-Driven Teamwork

Group assignments are a staple in higher education, providing opportunities to enhance collaboration and communication skills. However, we frequently encounter grievances related to group work as coordinators of large STEM units (800 students). Our student survey identifies work ethic and communication discrepancies between group members as barriers, often leading to uneven workloads, dissatisfaction, and diminished collaborative learning (Bacon et al., 1999). Common mitigation strategies to address work ethic discrepancies, like group charters and peer evaluations, often prove ineffective, since they could be ignored if not properly followed up or enforced (Chang & Brickman, 2018). Another barrier to group productivity lies in the team formation process itself. Traditional team formation methods – self-selection, random assignment, or instructor-led grouping – each present inherent limitation. Self-selection often results in homogenous groups susceptible to "groupthink" (Janis, 1982), limiting the diversity of thought essential for creative problem-solving. Random assignment, while seemingly impartial, can lead to imbalanced skillsets and an uneven distribution of workload (Bacon et al., 1999). While instructor-led grouping might seem ideal, it is logistically impractical within large cohorts (Decker, 1995). Computer-aided team selection offers a promising alternative. Its automated process streamlines team formation, generates diverse teams, and allows instructors to focus on teaching, despite concerns about increased workload (Cavanaugh et al., 2004). To address these group work challenges, we have developed a multi-pronged strategy integrating computer-aided team selection with a comprehensive follow-up and feedback framework. Our goal is to transform group work from a source of anxiety to a rewarding learning experience. Our approach combines technology with strategic interventions: Computer-aided Team Formation: We utilise Gruepr, an open-source team selection software (Hertz, 2021), to generate groups based on student preferences including work styles and meeting preferences (Cavanaugh et al., 2004). Gruepr integrates seamlessly with our learning management system, Canvas. Structured Communication, Collaboration, and Proactive Monitoring: Regular temperature-check surveys deployed through Canvas allow students to rate their group's progress and workload distribution. This data enables early identification of potential issues. To encourage group dialogue, the aggregated and anonymised outcome is shared with team members Facilitating Peer Feedback and Communication through SRES: The Student Relationship Engagement System (SRES), a learning analytics tool (Liu et al., 2017), was used to communicate aggregated peer feedback to individual group members, encouraging constructive dialogues regarding group progress. Our evaluation compared student performance, feedback, and complaints from the current year to previous years, alongside an estimation of staff workload. Preliminary findings demonstrate a reduction in staff time spent on group formation using Gruepr's automated grouping, highly contingent upon accurate survey design. Complaints, though still reported, were less severe, suggesting that regular temperature checks promoted proactive communication. Importantly, student feedback provided valuable insight, with many groups identifying the need for increased in-person interaction and improved scheduling strategies to accommodate diverse commitments. Our presentation will elaborate on these results, showcasing both successes and challenges, and propose a refined workflow to further enhance collaborative learning experiences.

Metatheoretical Distinctions in Theories of Functional Differentiation: Delineating Alternative Traditions

While the concept of functional differentiation is one of sociology’s oldest analytic tools, there is significant confusion about its meaning and purpose in the contemporary discipline. This article addresses one source of uncertainty: the conflicting array of ontological and methodological positions which are currently attached to the differentiation term. Drawing on Laudan’s philosophy of science, I argue that sociology does not house a unified program of differentiation theory, but is instead marked by at least two discrete traditions of differentiation thinking. Disentangling these traditions will, I contend, clarify our thinking, and help reduce the prevalence of internally contradictory conceptual models.

OpenOPAF: An Open Source Multimodal System for Automated Feedback for Oral Presentations

Providing automated feedback that facilitates the practice and acquisition of oral presentation skills has been one of the notable applications of multimodal learning analytics (MmLA). However, the closedness and general unavailability of existing systems have reduced their potential impact and benefits. This work introduces OpenOPAF, an open-source system designed to provide automated multimodal feedback for oral presentations. By leveraging analytics to assess body language, gaze direction, voice volume, articulation speed, filled pauses, and the use of text in visual aids, it provides real-time, actionable information to presenters. Evaluations conducted on OpenOPAF show that it performs similarly, both technically and pedagogically, to existing closed solutions. This system targets practitioners who wish to use it as-is to provide feedback to novice presenters, developers seeking to adapt it for other learning contexts, and researchers interested in experimenting with new feature extraction algorithms and report mechanisms and studying the acquisition of oral presentation skills. This initiative aims to foster a community-driven approach to democratize access to sophisticated analytics tools for oral presentation skill development.

Business Analytics in Customer Lifetime Value: An Overview Analysis

ABSTRACTIn customer‐oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision‐making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state‐of‐the‐art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan—one of the study's significant contributions—was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.

EcoCommons Australia Virtual Laboratories with Cloud Computing: Meeting Diverse User Needs for Ecological Modeling and Decision-making

Biodiversity decline and climate change are among the most important environmental issues society faces. Information to address these issues has benefited from increasing big data, advances in cloud computing, and subsequent new tools for analytics. Accessing such tools is streamlined by virtual laboratories for ecological analysis, like the ‘Biodiversity and Climate Change Virtual Laboratory’ (BCCVL) and ‘ecocloud’. These platforms help reduce time and effort spent on developing programming skills, data acquisition and curation, plus model building. Recently this functionality was extended, producing EcoCommons Australia—a web-based ecological modeling platform for environmental problem-solving—with upgraded infrastructure and improved ensemble modeling, post-model analysis, workflow transparency and reproducibility. We outline our user-centered approach to systems design, from initial surveys of stakeholder needs to user involvement in testing, and collaboration with specialists. We illustrate EcoCommons and compare model evaluation statistics through four cases studies, highlighting how the modular platform meets users’ needs.

ChatKG: Visualizing time-series patterns aided by intelligent agents and a knowledge graph

Line-chart visualizations of temporal data enable users to identify interesting patterns for the user to inquire about. Using Intelligent Agents (IA), Visual Analytic tools can automatically uncover explicit knowledge related information to said patterns. Yet, visualizing the association of data, patterns, and knowledge is not straightforward. In this paper, we present ChatKG, a novel visual analytics strategy that allows exploratory data analysis of a Knowledge Graph that associates temporal sequences, the patterns found in each sequence, the temporal overlap between patterns, the related knowledge of each given pattern gathered from a multi-agent IA, and the IA’s suggestions of related datasets for further analysis visualized as annotations. We exemplify and informally evaluate ChatKG by analyzing the world’s life expectancy. For this, we implement an oracle that automatically extracts relevant or interesting patterns, populates the Knowledge Graph to be visualized, and, during user interaction, inquires the multi-agent IA for related information and suggests related datasets to be displayed as visual annotations. Our tests and an interview conducted showed that ChatKG is well suited for temporal analysis of temporal patterns and their related knowledge when applied to history studies.

Big Data in Cloud Computing

The convergence of Big Data and cloud computing has revolutionized the way organizations process, store, and analyze large datasets. This paper explores the synergistic relationship between these two transformative technologies, highlighting their impact on business operations, decision-making, and innovation. By leveraging cloud platforms, enterprises can harness the scalability, flexibility, and cost-efficiency of distributed computing to manage vast volumes of data generated from diverse sources. Cloud-based big data analytics enables real-time insights, which drive strategic actions, improve customer experiences, and foster competitive advantages. The research delves into key cloud architectures, data management techniques, and analytics tools that underpin big data solutions, while addressing challenges such as data security, privacy, and compliance. Furthermore, the study reviews industry use cases across various sectors, illustrating how big data in cloud environments enhances productivity and innovation. The findings underscore the pivotal role of cloud computing in unlocking the full potential of big data, offering a roadmap for businesses aiming to capitalize on data-driven strategies.

Smart Attendance Management System with QR Code Authentication

Abstract: This paper presents the development of a Smart Attendance Management System (SAMS) designed to automate and streamline the process of tracking attendance in educational institutions and workplaces. The proposed system utilizes advanced technologies such as facial recognition and QR code to accurately and efficiently monitor attendance. By eliminating the need for manual entry, the system reduces human error, saves time, and enhances data accuracy. The SAMS also integrates with databases and analytics tools to provide real-time reporting and insights, helping institutions make data-driven decisions. The study outlines the system's architecture, implementation, and potential impact, demonstrating its ability to enhance operational efficiency and improve user experience in diverse environments

AI in customer feedback integration: A data-driven framework for enhancing business strategy

The integration of artificial intelligence (AI) into customer feedback systems has emerged as a transformative approach for businesses seeking to enhance their strategies and maintain a competitive edge. This review presents a data-driven framework that leverages AI to analyze, interpret, and act upon customer feedback, providing actionable insights for business decision-making. AI techniques such as natural language processing (NLP), machine learning (ML), and sentiment analysis allow companies to automate the feedback collection process and analyze vast amounts of data from diverse sources, including surveys, reviews, social media, and customer support interactions. The proposed framework facilitates real-time feedback analysis, enabling businesses to identify trends, customer preferences, and potential pain points more efficiently. By integrating AI with existing customer relationship management (CRM) systems, businesses can automate the categorization and prioritization of feedback, allowing for timely responses and more effective problem-solving. Furthermore, predictive analytics tools within the framework can forecast customer needs, allowing businesses to tailor products and services to meet evolving expectations. This framework also supports continuous improvement by enabling businesses to track the impact of changes implemented based on customer feedback. Additionally, AI’s ability to personalize the customer experience by recognizing patterns and individual preferences plays a crucial role in increasing customer satisfaction and loyalty. The data-driven insights generated through AI integration can guide businesses in refining their marketing, product development, and customer service strategies, leading to improved operational efficiency and better alignment with customer expectations. In conclusion, the integration of AI into customer feedback mechanisms represents a significant advancement for data-driven business strategy development. This framework not only enhances feedback accuracy and speed but also empowers businesses to deliver more personalized and customer-centric solutions.

Integrating FinOps in healthcare for optimized financial efficiency and enhanced care

The integration of Financial Operations (FinOps) in healthcare is increasingly recognized as a critical strategy for optimizing financial efficiency and enhancing patient care. This paper explores the principles and practices of FinOps, emphasizing its role in improving cost management, fostering financial transparency, and ensuring better resource allocation in healthcare organizations. By addressing traditional inefficiencies in healthcare financial management, FinOps promotes collaboration between finance and operations, enabling real-time financial decision-making that aligns with both operational goals and patient outcomes. Additionally, the paper outlines the challenges healthcare organizations face in managing rising costs and complex billing systems, and provides practical strategies for the successful implementation of FinOps, including the use of advanced technologies such as cloud-based platforms and data analytics tools. Ultimately, the adoption of FinOps not only enhances short-term financial efficiency but also contributes to the long-term sustainability and scalability of healthcare services.

Research Trends and Hotspots of Big Data in Ophthalmology: A Bibliometric Analysis and Visualization

ABSTRACT Purpose The burst of modern information has significantly promoted the development of global medicine into a new era of big data healthcare. Ophthalmology is one of the most prominent medical specialties driven by big data analytics. This study aimed to describe the development status and research hotspots of big data in ophthalmology. Methods English articles and reviews related to big data in ophthalmology published from January 1, 1999, to April 30, 2024, were retrieved from the Web of Science Core Collection. The relevant information was analyzed and visualized using VOSviewer and CiteSpace software. Results A total of 406 qualified documents were included in the analysis. The annual number of publications on big data in ophthalmology reached a rapidly increasing stage since 2019. The United States (n = 147) led in the number of publications, followed by India (n = 77) and China (n = 69). The L.V. Prasad Eye Institute in India was the most productive institution (n = 50), and Anthony Vipin Das was the most influential author with the most relevant literature (n = 45). The electronic medical records were the primary source of ophthalmic big data, and artificial intelligence served as the principal analytics tool. Diabetic retinopathy, glaucoma, and myopia are currently the main topics of interest in this field. Conclusions The application of big data in ophthalmology has experienced rapid growth in recent years. Big data is expected to play an increasingly significant role in shaping the future of research and clinical practice in ophthalmology.

The Post-transplant Lymphoproliferative Disorders-Metagenomic Shotgun Microbial Sequencing (PTLD-MSMS) Study Methods and Protocol.

Post-transplant lymphoproliferative disorders (PTLDs) remain a feared complication of transplantation, with significant morbidity and mortality. The oncogenic Epstein-Barr virus (EBV) is a key pathogenic driver in 50%-80% of cases. Numerous prognostic indices, comprising multiple clinical, epidemiological and tumor characteristics, including EBV tumor positivity, do not consistently associate with worse patient survival, suggesting a potential role for EBV genome variants in determining outcome. However, the precision medicine tools for determining if a viral genome variant is pathogenic are very limited compared with human genome variants. Further, targeted studies have not implicated a specific viral etiological agent in EBV-negative PTLD. Using novel cutting-edge technologies, we are extracting viral nucleic acids from formalin-fixed, paraffin-embedded archived, or frozen PTLD tissues or plasma, to test for all vertebrate viruses simultaneously in an unbiased fashion, using metagenomic shotgun sequencing (MSS). We are collecting such samples from multiple transplant centers to address the following specific aims and close the following knowledge gaps: (1) Validate our novel observation that PTLD tissue positivity by MSS for anellovirus (and confirmed by PCR) serves as a biomarker for higher transplant recipient mortality after the diagnosis of PTLD; (2) determine the role of other oncogenic viruses in EBV-negative PTLD by unbiased MSS of multiple viral groupings, confirmed by other techniques; and (3) develop the necessary computational, algorithmic and software analytic tools required to determine association of EBV genome variants with worse presentations or outcomes in PTLD. Study completion will contribute to better patient care and may provide avenues for novel therapies.

Impact of an enterprise controlled substance management system on labor and inventory costs.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To evaluate the impact of an enterprise controlled substance (ECS) management system with integration of analytics software on labor requirements and inventory cost within a health-system pharmacy. A prospective, pre-post observational study was designed to assess the impact of implementing a solution that connects disparate systems with the integration of analytics software. Three study modules were implemented over approximately 18 months. The intervention consisted of implementation of new CS vaults, a centralized server, an automated central medication inventory system, and inventory optimization analytics software. The number of transactions and time spent on CS reports were compared before and after implementation to determine labor and inventory efficiencies. Both of the study facilities had a decrease in CS daily stockouts and an increase in inventory turns compared to baseline, while the total number of transactions (vends) at the central vault and decentralized dispensing cabinets increased. The addition of analytics allowed for establishment of informed changes to periodic automated replenishment levels. Additionally, both facilities saw a reduction in the number of expired medications, and there was subsequently a reduction in the total reverse distributor costs. Finally, both facilities had a reduction in the amount of time spent on manual tasks associated with reconciling and managing discrepancies. An inventory management system integrated with an advanced analytics tool provided a reduction in the time spent on receiving, storing, and reconciling CS records while the number of transactions increased. The ECS solution enhanced the visibility of the chain of custody while closing the loop between reporting and receiving inventory, eliminating or reducing the frequency of manual processes.

Opportunity lost: addressing DEI in academic conference design practices

PurposeAttending academic conferences is important for career progression. However, conferences can be experienced as exclusionary by historically marginalised groups. Non-attendance through exclusionary event design thus has far-reaching consequences, which is a social and structural justice issue. This research therefore aims to shine a light on event design in academic conferences, and its relationship to diversity, equity and inclusion (DEI).Design/methodology/approachThrough empirical study, we address the question “Are academic conferences communicating a commitment to DEI for delegates from historically marginalised groups, and if so, how?”. Using a content protocol (Loh et al., 2022) and signalling theory (Bradley et al., 2023), we analyse the publicly available conference materials of 70 tourism, leisure, events and hospitality academic conferences planned or convened between 2023 and 2025 to assess DEI discourses and practices for evidence of conference design that fosters inclusivity, belongingness and therefore addresses justice.FindingsReferences to one or more elements of DEI were found in the discourses of around one quarter of the conferences. However, for BIPOC [1], precarious academics, those from the LGBTQIA+ or disability communities, those with caring responsibilities, those on low or no incomes or with visa requirements, and to a large degree still for women, there were few signs within conference design practices that could be construed as conveying a welcoming culture. We therefore argue that academic conferences within the fields of tourism, leisure, events and hospitality are perpetuating inequity, exclusion and injustice through failing to give full consideration to whom their event design practices are inviting to participate. This is an opportunity lost, and we provide a checklist for conference organisers to help them communicate that historically marginalised academics are welcome and belong at their event.Originality/valueWe believe this is the first study to take a DEI lens to an examination of academic conferences, and apply a content protocol and signalling theory as analytic tools in the process.

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review.

There is an emerging need for evidence-based approaches harnessing large amounts of health care data and novel technologies (such as artificial intelligence) to optimize public health policy making. The aim of this review was to explore the data analytics tools designed specifically for policy making in noncommunicable diseases (NCDs) and their implementation. A scoping review was conducted after searching the PubMed and IEEE databases for articles published in the last 10 years. Nine articles that presented 7 data analytics tools designed to inform policy making for NCDs were reviewed. The tools incorporated descriptive and predictive analytics. Some tools were designed to include recommendations for decision support, but no pilot studies applying prescriptive analytics have been published. The tools were piloted with various conditions, with cancer being the least studied condition. Implementation of the tools included use cases, pilots, or evaluation workshops that involved policy makers. However, our findings demonstrate very limited real-world use of analytics by policy makers, which is in line with previous studies. Despite the availability of tools designed for different purposes and conditions, data analytics is not widely used to support policy making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy making. Based on the findings, we make suggestions for researchers developing digital tools to support public health policy making. The findings will also serve as input for the European Union-funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform.

Halkla İlişkilerde Tüketici Aktivizminin Rolü: Şirketler için Yeni Bir Paradigma

Consumer activism refers to consumers collectively acting to create change in companies, and this phenomenon has been further strengthened by the new communication tools and social media brought by the digital age. In the past, companies largely controlled trends and consumer preferences with traditional advertising and marketing strategies. However, consumers’ increasing awareness and organizational skills have changed these power dynamics. Now, consumers can make their voices heard through social media platforms and digital tools and become an effective force for social change. However, consumer activism can also create a potential public relations crisis for companies. The public’s demand for policy and practice changes in an organization can threaten the reputation and financial performance of companies. Therefore, managing consumer activism has become a fundamental element of public relations strategies. Companies should use public relations tools to protect their brand reputation and maintain consumer trust by developing proactive and reactive strategies. While proactive public relations emphasizes brand values and transparency before crises, reactive public relations offers fast and effective responses during crises. Integrating consumer activism into brand strategy can provide significant benefits by aligning a company’s core values with the concerns and expectations of its consumers. Social media is an effective tool for engaging with activist consumers. Using social media platforms, companies can listen to consumer feedback, engage in relevant online conversations, and address concerns. Additionally, measuring the impact of consumer activism on brand perception is also critical for companies. Using analytics tools, companies can track consumer feedback and interactions. This provides important data to improve future strategies and better respond to the demands of activist consumers. Consumer activism is driving organizations toward excellence by creating a more engaged and responsive approach to public relations. Key Words: Public Relations, Consumer, Activism, Brand, Paradigm.

Impact of Social Media Marketing on Consumers in Philippines

Purpose: The aim of the study was to analyze the impact of social media marketing on consumers in Philippines. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The impact of social media marketing on consumers in the Philippines reveals that platforms like Facebook, Instagram, and Twitter significantly influence purchasing behavior, especially among younger audiences. Studies show that Filipino consumers are highly engaged with brands on social media, where personalized content, influencer marketing, and customer interaction drive brand loyalty and product awareness. Social media also facilitates quicker decision-making, as consumers rely on peer reviews and recommendations before making purchases. However, challenges include issues with data privacy and the oversaturation of promotional content, which can lead to consumer fatigue. Unique Contribution to Theory, Practice and Policy: Theory of planned behavior (TPB), uses and gratifications theory (UGT) & social influence theory may be used to anchor future studies on the impact of social media marketing on consumers in Philippines. Companies should invest in analytics tools to understand consumer preferences, enabling them to create more personalized and engaging content. Policymakers should develop regulations that ensure transparency in social media advertising and influencer endorsements, particularly in areas like disclosure of paid partnerships.

Using machine learning for the assessment of ecological status of unmonitored waters in Poland

Advancements in Artificial Intelligence (AI) technology allow for development of new tools for analytics and management which present new opportunities in field of environmental protection. The following study showcases usage of Machine Learning (ML) techniques as a complementary method for water status assessment of water bodies. Since the main goal of Water Framework Directive (WFD) is to improve the quality of water and reach good status in all the water bodies across Europe intensive monitoring program was launched together with water status assessment procedure. Based on requirements of the European Union’s WFD concerning ecological status assessment it is presented how ML can be used for assessment of Polish unmonitored river water bodies. Due to the absence of monitoring data, the foremost challenge lay in securing relevant alternative data which was set to be anthropogenic pressures. The pivotal solution was implementation of ML techniques which enable processing of seemingly unrelated information concerning pressures in the catchment. Decision Tree, Random Forest, KNN, Support Vector Machine, Multinomial Naive Bayes, XGBoost models have been tested and the results indicated most suitable techniques. Study shows highest efficiency of XGBoost and Random Forest algorithms for classification of unmonitored water bodies. The models were compared by their overall accuracy (OA) reaching approximately 93% for binary classification and 72% for comprehensive classification as well as partial class accuracies and the Probability of Misclassification (PoM) parameter. The analyses demonstrates a practical application of AI in assessment of unmonitored water bodies in case of binary classification used for reporting water status objectives of WFD as well as possible usage of full classification for planning and operational uses. OA and PoM are postulated as the best measures of goodness of classification.

Basics of Internet of Things

The Internet of Things (IoT) is a rapidly growing technological paradigm that enables the interconnection of physical devices, sensors, and systems through the internet. IoT allows devices to collect, exchange, and act on data autonomously, creating seamless communication between the digital and physical worlds. It has applications across diverse domains, including smart homes, healthcare, transportation, agriculture, and industrial automation. IoT ecosystems involve key components like sensors, actuators, cloud platforms, communication protocols, and analytics tools. The technology's benefits include improved efficiency, real-time monitoring, automation, and enhanced decision- making through data analytics. However, IoT also presents challenges related to data security, privacy, interoperability, and scalability. As the number of connected devices continues to grow, advancements in 5G, artificial intelligence, and edge computing are expected to drive the evolution of IoT, making it a cornerstone of future smart environments. This abstract provides a concise overview of the IoT landscape, highlighting both its transformative potential and associated challenges. Keywords: IOT (Internet of Things) , Smart Devices, Sensors and Actuators, Connectivity, Real-time Monitoring, Automation, Cloud Computing, Edge Computing, 5G Networks, Interoperability.