Authors - Ubaydullo Vakhabovich Gafurov, Nargiza Ruzibaeva, Khujayar Musurmonovich Shennayev, Nosir Mahmudovich Mahmudov, Saodat Sadriddinova, Alisher Bakberganovich Sherov, Nilufar Karimova Abstract - The sustainable development of tourism enterprises increasingly depends on their capacity to implement innovative activities supported by efficient financial mechanisms. However, traditional financing models often fail to provide sufficient flexibility and accessibility for innovation-driven tourism firms, particularly small and medium-sized enterprises. This study proposes a digital financial ecosystem model designed to enhance the financing of innovative activities in tourism enterprises through digitalization instruments. The research integrates concepts of digital finance, FinTech platforms, data-driven credit assessment, and smart contract mechanisms into a unified systemic framework. Using a system dynamics modeling approach combined with structural analysis, the study develops a conceptual and quantitative model linking digital financial infrastructure, access to alternative funding sources, and innovation performance indicators. Empirical validation is conducted using survey data collected from tourism enterprises and analyzed through structural equation modeling. The results demonstrate that digital financial ecosystem components significantly improve funding accessibility, reduce transaction costs, and increase innovation intensity. The findings highlight the mediating role of digital maturity in strengthening the relationship between financial accessibility and innovation outcomes. The proposed model contributes to the theoretical development of digital transformation in tourism finance and offers practical implications for policymakers and enterprise managers. The study provides a scalable framework for enhancing innovation financing in digitally transforming tourism markets.
Authors - Heidi Koivisto, Sari Ahonen Abstract - The integration of AI-assisted code generation tools in software development has the potential to significantly improve productivity and code quality. This paper presents a comparative study of several leading AI tools, including GitHub Copilot, Amazon Q, GitLab Duo, and Claude Code, in the context of software quality assurance (QA). These tools are evaluated based on their ability to generate test cases for a sample application, focusing on metrics such as test coverage, accuracy, and maintainability. Our findings provide insights into the strengths and limitations of each tool, offering guidance to practitioners seeking to take advantage of AI in their QA processes. The results indicate that while AI tools can accelerate test case generation and improve coverage, careful consideration is needed to ensure the generated tests are relevant and maintainable in the long term.
Authors - Avanti Chokhare, Reena Satpute Abstract - With the world's population moving from rural to urban areas, urban infrastructure including, but not limited to, transportation systems, energy re-sources and public service delivery are all strained in their ability to cope with additional residents. Urban management techniques that have historically worked in managing urban growth are no longer sufficient to meet the demands of com-plex modern cities. The Internet of Things (IoT), a transformative technology that allows real-time data collection, enables smarter decision-making and automates many of the services within urban areas through a variety of connected sensors, devices and digital platforms. The purpose of this study is to analyze how IoT will enable the creation of sustainable and efficient smart cities by encompassing multiple transportation management systems, energy efficiency improvements, and environmental monitoring, health, education and digital governance administrative systems. Additionally, the study investigates additional enabling technologies such as Artificial Intelligence (AI), Edge and Cloud Computing, Block-chain and other technologies that can improve the overall functionality of the IoT. Finally, this paper investigates key challenges associated with the implementation of IoT applications in urban areas, including security, reliability and data privacy issues. It is clear that the implementation of standardized frameworks, scalable architecture and phased implementation strategies will be key components in the successful implementation of smart cities. Responsible adoption of IoT technology will lead to improved environmental sustainability, operational efficiency and citizen quality of life in urban areas.
Authors - Joseph Afriyie, Stephen Opoku Oppong, Benjamin Ghansah, Daniel Kobina Danso Essel, Dickson Keddy Wornyo, Ephrem Kwaa Aidoo Abstract - The COVID-19 pandemic forced educational institutions to adopt online learning, which resulted in expanded digital spaces that cybercriminals used to launch phishing attacks against students, faculty, and institutional systems. This research article provides a comprehen-sive literature review that evaluates machine learning techniques for phishing detection in online educational settings. The PRISMA guidelines were used to select 40 studies from 2013 to 2023 after researchers examined publications retrieved from IEEE Xplore, SpringerLink, and Google Scholar. The review analyzes various digital education ecosystems through its examination of algorithmic methods and datasets, performance evaluation metrics, and detection framework de-signs that universities use to defend against phishing attacks. The research shows that Random Forest and Gradient Boosting, together with deep learning methods, which include Convolutional Neural Networks, Long Short-Term Memory networks, and Recurrent Neural Networks, deliver superior detection performance reaching over 90% accuracy in most scenarios. Educational in-stitutions encounter three primary challenges, which include implementing real-time systems to combat emerging phishing techniques, ensuring dataset compatibility with various environments, and managing their restricted resource availability. The study establishes that institutions need to create technical detection frameworks that work together with user training programs to establish better institutional protection measures.
Authors - Bullet Tiwari, Reena Satput Abstract - The Internet of Things (IoT) systems are becoming numerous, trans-forming our virtual world by continually gathering information, linking equipment, and automating most of the spaces. However, the size, decentralization, and dispersal of the IoT networks cause grave droughts with reliability, security, and workability. The current rule-based surveillance tools are not able to handle the dynamism and volume of data generated by drastically many IoT devices. The paper describes a machine learning (ML) system that predicts the performance of applications, issues, and predicts failures of devices in IoT settings. It compares the methods of supervised, unsupervised, and deep learning and examines their performance in the constraints of computing power, delay and energy. It talks about the trade-offs between centralized and decentralized learning in which clouds and edges are used respectively to decide on the most suitable deployment. The framework has security, privacy, and sustainability design issues, as well. The suggested solution is expected to enhance IoT resiliency with the help of predictive intelligence to manage issues before they happen and increase the overall stability of the industry, healthcare, and smart city environments.
Authors - Roseline Oluwaseun Ogundokun, Rotimi-Williams Bello, Pius Adewale Owolawi, Chunling Tu, Etienne A. van Wyk Abstract - Extramarital affairs can undermine trust and lead to relational break-down, yet the ability to anticipate risk factors remains limited. Recent studies have used deep learning approaches to predict infidelity, achieving high classification accuracy but at the cost of interpretability and resource requirements. This article proposes a novel research to examine whether simple, interpretable mod-els, k-nearest neighbours (KNN), linear regression (LR) and support vector regression (SVR), can predict the amount of time individuals spend in extramarital affairs using readily available socio-demographic and relational features. Using the well-known affairs dataset comprising 6,366 observations and nine variables, we apply feature engineering, cross-validation training and regression-based evaluation to compare model performance. Our findings indicate that although KNN outperforms LR and SVR in terms of accuracy, all models struggle to capture variance; mean squared error (MSE) values remain high, and the coefficient of determination (đť‘…2) values close to zero. We discuss the implications for predictive counselling and outline future research directions.
