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.
Authors - Manar Eloued , Narjes Benameur , Sonia Esseghaier, Salam Labidi Abstract - Magnetic Resonance Elastography (MRE) is a novel, non-invasive im-aging technique for assessing liver stiffness. However, the lack of standardization introduces variability in measurements. The Manual selection of the Region of Interest (ROI) remains subjective and operator-dependent, often including areas with blood vessels or poor wave propagation, which can compromise measurement accuracy. This study proposes a deep learning- based approach to automatically identify an optimal region for liver stiffness measurement (LSM). A total of 160 MRE ex-ams, comprising paired magnitude and wave attenuation images from both healthy individuals and patients with liver disease, were used. A 3D U-Net architecture was trained to segment the liver, blood vessels, gallbladder, and biliary ducts from magnitude images, as well as regions of good wave propagation from attenuation images. The final ROI was obtained by intersecting these segmented regions. The model performance was evaluated on a separate test set using the Dice Similarity Coefficient (DSC), paired Student’s t-test, and Bland-Altman analysis. The resulting LSM region achieved a DSC of 0.89. The t-test yielded p = 0.68, indicating no significant difference between the automated and manual ROIs (p > 0.05). This automated pipeline reduced analysis time from approximately 20 minutes manually to less than 10 seconds automatically, while ensuring reproducibility and reducing operator dependency in MRE by standardizing ROI selection while maintaining diagnostic accuracy. It offers a promising solution to improve the reliability of LSM, particularly for longitudinal follow-up.
Authors - Hector Rafael Morano Okuno Abstract - One of the applications of LLMs (Large Language Models) has been their use as assistants, enabling users to perform specific tasks via prompts, from solving mathematical problems to generating images or videos. This article aims to explore the capabilities of an LLM in generating scripts for the API (Application Programming Interface) of the CAD software Fusion 360, identify the types of geometries it can create, and determine whether it can reproduce images in CAD models. This work was developed during the Manufacturing Systems Automation course for Mechatronics Engineering students, with the intention of introducing them to the use of LLMs in their field. Among the find-ings, it was determined that the user must be familiar with the Fusion 360 API to correct potential errors in the scripts generated by the LLM. Furthermore, the user must be able to specify, via prompts, the characteristics of the parts to be modeled, ensuring that the specifications are compatible with the instructions Fusion 360 understands. Regarding students' experience with LLMs, they found them useful, as they saved time in designing components that require complex automation systems.
Authors - Zilola Mamatvaliyevna Aliyeva, Nigora Primova, Dildor Abduraxmanovna Shadibekova, Malika Akbarova, Azizbek Mahkamov, Gulchehra Raxmatjonovna Xusanova, Shoh-Jakhon Khamdаmov Abstract - The construction industry plays a strategic role in Uzbekistan’s economic development; however, it continues to face challenges related to cost overruns, project delays, and limited financial transparency. Digital transformation offers new opportunities to enhance economic efficiency and project management performance. This study examines the impact of digitalization on construction economics in Uzbekistan, focusing on the implementation of Building Information Modeling (BIM), digital cost estimation systems, electronic procurement platforms, and enterprise resource planning (ERP) solutions. The research develops a conceptual model linking digital adoption level, cost control effectiveness, project performance, and financial outcomes. A quantitative survey of construction companies operating in Uzbekistan was conducted, and Structural Equation Modeling (SEM) was applied to test the proposed relationships. The findings indicate that higher levels of digital integration significantly improve cost estimation accuracy, reduce budget deviations, and shorten project completion time. Digital procurement systems also enhance financial transparency and reduce operational inefficiencies. The study provides empirical evidence that digital transformation positively influences economic performance in Uzbekistan’s construction sector. The results contribute to construction economics literature and offer policy recommendations for accelerating digital adoption in emerging markets.
Authors - Jorge Duque, Antonio Godinho, Jose Moreira, Firmino Silva Abstract - Student dropout in higher education remains a persistent academic, institutional and social challenge, requiring evidence-informed responses. This paper develops and evaluates an explainable machine learning artefact for early dropout-risk identification and for translating predictions into tiered institutional interventions. The study follows six phases of Design Science Research and uses the public Predict Students' Dropout and Academic Success benchmark, with 4,424 students and 37 variables. The pipeline integrates one-hot encoding, derived features, stratified validation, SMOTE applied only inside training folds, Random Forest, XGBoost and SVM as base learners, stacking with a logistic meta-learner and SHAP explanations. The final ensemble achieved 96.4% accuracy, 0.965 weighted precision, 0.964 weighted recall, 0.964 weighted F1-score and 0.99 weighted AUC. The contribution lies in combining performance, interpretability, governance and responsible human intervention.
Authors - Hasna Noushad, Geetha KN Abstract - The brain tumor remains a serious health concern and diagnosis in the primary stage is mandatory for effective treatment. Medical image analysis plays a vital role in understanding the underlying disturbances, monitoring, treatment planning, and intervention strategies. Graph theory is one of the popular techniques used for medical image analysis. The study focuses on the introduction of a systematic approach by using the application of vertex addition of graph theory to synthetically construct a glioma brain network utilizing the normal and abnormal brain Magnetic Resonance Image (MRI). The paper presents the idea of the initial emergence and growth of tumor from a graphtheoretical perspective. Comparative analyses are carried out between normal and abnormal brain network due to the growth of glioma. Results demonstrate that the presence of strong structural deformations and alterations in brain network due to the introduction and growth of glioma. In addition, the article also examines the corresponding increase in the correlation values of the tumor with other normal regions of brain as the tumor grows. This work provides a robust foundation for future studies in epidemiological modeling, machine learning and deep learning methodologies where lack of required data is an issue.
Authors - A Aruna kumari, Sri Vishnu Prabhu Gudavalli, Tamminana Visweswari Abstract - Diabetic Retinopathy (DR) is one of the biggest contributors of visual impairment and blindness in diabetic patients who do not receive proper measures and treatment at the early stages. Due to the ever rising instances of diabetes by the day, more concern has been raised on the effectiveness of methods of effective, scalable and early diagnosis. The given paper is a proposal of a deep learning-based algorithm of DR diagnosis, specifically, the algorithm named Patch-Wise Segmentation and Classification using Convolutional Neural Networks (CNNs). The system in this case is in contrast to the traditional systems, which require the entire retina image to be processed simultaneously by the system, whereby high-resolution fundus photographs are broken into patches. The model addresses each patch individually in order to have the model closely observe minute-scale details and sensitive pathological changes such as hemorrhages and exudates. Patch-wise processing dramatically enhances the capacity of reporting early and mild cases of DR that are hard to recognize in the fullimage processing due to intricacy of an image and noise. The CNN architecture is additionally medical image particular and operates by use of layers and regularization methods so as to permit not only accuracy but also generalization over a wide range of datasets. As shown in the results of the experiments, the patchwise technique performs better in comparison with the traditional ones, i.e., sensitivity, specificity, and classification accuracy on each of the stages of the DR. In addition, the system is fully automated and stable that minimizes the use of human marking and offers the facility to operate worldwide
Authors - Gavin Singh Pandha, Umar Khokhar, Binh Tran Abstract - This research initiative investigates the growing role of artificial intelligence (AI) in the automation of cyber-attacks and its emerging impact on modern cybersecurity. This study explores how AI models can be used to per-form web application intrusions by simulating attacks against deliberately vulnerable applications. Performance metrics from these simulations are compared with traditional, manually executed intrusion techniques, in addition to examining real world cases for AI misuse, ethical concern, industry standard, and the growing risk of autonomous threat actors
Authors - M. V. Rama Sundari, Bhuvan Unhelkar, Pravin Kshirsagar, Supriya Nandikolla Abstract - The management of nutrient content in soil is vital for improving productivity of crops, as well as sustainable agriculture. Conventional processes of establishing the best ratios of macronutrients tend to overlook intrinsic relation-ships that exist among soil properties, crop, weather. Main purpose is to come up with a smart prediction system that will help forecast Macronutrient needs by infusing particular domain-specific expertise in agriculture into machine learning models to improve interpretability and predictive accuracy. The suggested methodology utilises a Graph Convolutional Network (GCN) to simulate spatial and relational relationships amongst soil, crop and environmental parameters. To provide the model with a better semantic understanding, a Knowledge Graph (KG) is created to encode the relationships between domains. Embedding algorithms, such as TransE and DistMult, are then added to the GCN to form a KG-embedded GCN model that can learn feature-based as well as semantic relationships to predict nutrients. The experimental analyses on the ICFA Crop Recommendation dataset reveal that the baseline GCN got the R² scores of 0.806, 0.729, and 0.768, and the TransE GCN and DistMult GCN models have been advanced to the R² scores of 0.808-0.821, 0.746-0.762, and 0.800-0.814 for Nitrogen, Phosphorus and Potassium respectively. These findings indicate that the predictive strength is greatly advanced by the incorporation of domain knowledge. The model can, however, perform differently on unknown crop varieties and soils, which suggests that more work needs to be done in the future on larger and region-specific datasets.
Authors - Antonio Cortes Castillo Abstract - The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed data center operations. The widespread adoption of AI services has introduced new requirements for hosting AI systems, prompting significant modifications in the design and construction of modern data centers. As a result, data center operators must implement comprehensive strategies to address the challenges associated with AI integration. This study explores the application of machine learning techniques using neural networks and Multilayer Perceptron (MLP) models for data center optimization. The research focuses on critical metrics, including power consumption, liquid cooling, fiber-optic systems, and the number of fiber-optic routing paths, which pose significant challenges for data center operators. Experimental results are analyzed using simulation tools, including SPSS, to demonstrate enhancements in data center performance and efficiency.
Authors - Hera Khairunnisa, Naomi Helena Elizabeth, Dwi Kismayanti Respati, Ayatulloh Michael Musyaffi, Gentiga Muhammad Zairin Abstract - This study presents a bibliometric analysis of the literature on accounting and non-profit organizations (NPOs) based on 248 documents retrieved from the Scopus database. Data were processed and visualized using Biblioshiny (R Studio) and Scopus web analysis. The findings indicate a significant growth in publications since 2000, peaking in 2020–2024. Accounting, Auditing and Accountability Journal emerged as the most dominant source, and the distribution of journals is consistent with Bradford's Law. The United States and United Kingdom lead in scientific contributions, while Indonesia shows a growing presence. Keyword analysis reveals a shift toward contemporary themes such as blockchain, sustainability, and social accounting. This study provides a systematic mapping of the intellectual landscape of NPO accounting research and identifies opportunities for future investigation.
Authors - Himangshu Sarma, Madhumita Banerjee, Chandrajit Choudhury Abstract - Diabetic Retinopathy starts at a light level with no visible symptoms, but it can lead to severe pain and blindness as the disease progresses. Clinically, DR is diagnosed by looking for retinal detachment or utilizing imaging techniques like fundus imaging or optical tomography. The Early Diabetic Retinopathy Study is one of the established DR staging schemes. Image Processing has played a significant role in improving the methods used to detect the disease automatically. It has a huge role in assisting ophthalmologists in the screening process, as manually screening by ophthalmologists consumes more time, sometimes there may also be error. Although there are a number of algorithms used in detection, therefore, in this work we have studied and implemented various DR detection methods. To differentiate among various classes, we also prepared KAGGLE APTOS dataset containing the GLCM, LTP, GLRLM, LMeP, CLBP, CSLBP and LBP features extracted from the image dataset. And the classification is performed in the dataset, whilst achieving an accuracy of 97% on testing dataset and 96% for training dataset using SVM multi classifier.
Authors - Najera R. Umpar, Minsoware S. Bacolod Abstract - In this study, the readiness of teachers in adopting Artificial Intelligence (AI) in teaching and learning, and the variables that influence their acceptance or resistance towards it, were investigated. Following a qualitative research approach, semi-structured interviews were employed to gather the data. It was found that a range of factors including generation of teachers, teaching discipline or specialization, institutional support, and ethical issues influence teachers' readiness in implementing AI in the teaching process. Young teachers, along with those who are specialized in STEM subject area, expressed confidence, preparedness, and enthusiasm in embracing AI in teaching. Experienced teachers, and teachers who taught other subject area, expressed concerns toward relevance and teachers' autonomy. Institutional support (were considered as a factor that would significantly impact teachers' readiness toward AI integration. Ethical concern such as student privacy, bias algorithm, and student monitoring have also contributed to teachers' beliefs on AI implementation. More important, the study identified "hybrid readiness" where teachers believe AI can serve as the "co-teacher" of them and contribute to individual learning and pedagogical practice. It suggests that there are variety of factors influencing the teachers' readiness toward AI implementation thus it requires more comprehensive planning in order to have more efficient integration of AI in the classroom. It is found that teachers' readiness is still not homogeneous and context dependent, therefore differentiated training and education as well as support policy are necessary to enhance teacher readiness and promote the integration of AI in education.
Authors - Kalinka Kaloyanova, Elitza Kaloyanova Abstract - Despite the increasing use of artificial intelligence (AI) in healthcare, clinician adoption of AI tools is still obstructed by algorithmic aversion, which reflects scepticism about the results of AI use. This article examines how hospitals can enhance AI adoption by strengthening AI competencies in physicians and mitigating mistrust through a systematic, data-driven approach. A review of behavioral studies reveals barriers to AI adoption across technical, cognitive, organizational, and ethical domains. A framework is proposed that focuses on integrating individual clinician competencies with structured strategies implemented by hospitals that support workflow and create conditions for continuous learning. The implementation of data-centric strategies, explainable AI tools, competency programs, simulation training, and interprofessional collaboration is recommended to increase trust in AI in medicine, support its ethical use, which ultimately leads to safer healthcare delivery.
Authors - Md Manirul Islam, Umme Salsabil, Md. Mushfiqur Rahman, Sazzad Hossain Abstract - This paper presents a compact identity-verification architecture for private web and Internet of Things (IoT) deployments that require tamper evidence without the operational overhead of a full blockchain. The framework separates credential verification from profile-integrity verification across multiple stores: a credential store, a protected-profile store, a reference integrity store, and a key store. Credentials are protected with Argon2id-based verifiers, while protected profile records are bound to entity identifiers, timestamps, and version counters through HMAC-SHA-256 reference tags. Unlike scan-heavy hash-only workflows, the proposed design performs direct indexed lookup by entity identifier and then verifies integrity through a keyed comparison step, improving both security posture and scalability. The same logic can be deployed behind HTTPSbased web services and MQTT-over-TLS IoT gateways. A reference prototype and benchmark study over datasets of 1,000 to 10,000 entities show that the indexed login path remains nearly size-stable, with median successful login latency around 1.68-1.69 ms under a development-profile Argon2id configuration, while a scan-based baseline login path grows from 0.92 ms to 6.90 ms over the same range. Injected profile tampering was detected in all benchmarked trials. The resulting framework offers a pragmatic middle path between conventional centralized login and heavyweight distributed-ledger authorization for institutions that prioritize local autonomy, compartmentalization, and data-integrity assurance.
Authors - MD Junayed Talukdar, Khosro Salmani Abstract - Artificial Intelligence (AI) systems are widespread across fields such as healthcare, finance, employment, and criminal justice, with a substantial impact on the lives of individuals and society. However, AI systems have been shown to perpetuate existing social inequalities, particularly through biases that are not easily discernible. Such biases are embedded in the technical and social structures of AI systems, posing a direct challenge to the principles of Equity, Diversity, and Inclusion (EDI) understood as the acknowledgment of differences among individuals, fairness and equal access, and the valuation of all participants. This study argues that fairness in AI cannot be achieved by focusing solely on technical aspects, necessitating a holistic approach. To investigate this, computational content analysis was applied to 360 occupational narratives generated by ChatGPT across nine professions and four geographic regions (Canada, Germany, India, and Bangladesh) using explicitly gender-neutral prompts. The analysis examined whether AI-generated narratives associate professions predominantly with one gender, and whether such patterns remain consistent across regions. Findings reveal that gender bias persists despite neutral prompting, with male-coded protagonists dominant in technical and manual labor professions and female-coded protagonists dominant in caregiving roles. Although regional conditions influenced the magnitude of gender imbalance, the direction of occupational gender patterns remained largely consistent across all four regions. This study identifies the empirical foundations necessary for future EDI-AI co-design frameworks, outlining the sociotechnical dimensions that such frameworks must address to be effective.
Authors - Yassine Lkhalidi, Mohamed Lkhalidi, Hatim Kharraz Aroussi, Achraf Tifernine Abstract - IoT device authentication remains vulnerable to credential theft and physical-layer impersonation, particularly where resource constraints preclude full PKI deployments. Existing approaches address subsets of this problem: RF fingerprinting exposes templates in plaintext, while zero-knowledge proof (ZKP) schemes authenticate static keys without binding to physical hardware. We propose ZK-RFAuth, a framework integrating Siamese CNN-based RF fingerprinting, Groth16 ZKP embedding verification, and Proof-of-Authority blockchain logging. A device’s hardware imperfections are captured as a compact embedding; a Groth16 circuit proves the L1 distance between a fresh embedding and the registered template falls below a predefined threshold, without revealing either vector. Evaluated on WiSig (28 WiFi transmitters, 224,000 I/Q frames), ZK-RFAuth achieves 91.4% closed-set accuracy, 2.25% Equal Error Rate, and 70.8% rogue rejection at the 95th-percentile operating threshold, requiring only 972 R1CS constraints for 144-byte proofs verified in approximately 3 milliseconds. ZK-RFAuth is the first framework providing physical-layer identity, embedding-level zero-knowledge privacy, open-set rogue detection, and immutable audit logging simultaneously.
Authors - Aryan Sharma, Dipali Baviskar Abstract - Modern computer networks are often equipped with an intrusion detection system (IDS) to detect malicious activities or cyber-attacks. Such a system must have high accuracy on known attacks, and at the same time it must be able to generalise to previously unseen attacks. However, supervised classifiers fail to generalise to new situations because they learn to map input data to output labels under a specific training distribution, and they perform poorly under a different test distribution, which is called distributional shift. In this paper, we propose a gating-based hybrid IDS that combines supervised classifiers with anomaly detectors. The gating network restricts the influence of the anomaly component to the uncertain prediction zone, i.e., the region of the output space where the classifier is uncertain, defined by a probability range of (0.15, 0.75)], and prevents unsupervised noise from affecting the confident supervised decisions. We evaluate the performance of our proposed system on three different test scenarios using the CIC-IDS-2017 dataset. The first test scenario consists of eight known attacks for which we train the classifiers on the corresponding training data, and then we test them on the corresponding test data. The second test scenario is an out-of-distribution stress test, in which we use 99% benign traffic and add DDoS and PortScan attacks to it, and test whether the system is able to detect them. The third test scenario is a zero-day test scenario in which we test the system on a previously unseen SQL Injection attack. Our findings are as follows. First, the Gating Hybrid RF+AE achieves an F1-score of 0.9778 and a precision of 0.9924 on the eight known attacks, which outperforms the standalone RF classifier with an F1-score of 0.9750. Secondly, on the out-of-distribution test scenario, both the RF and GBT classifiers fail to detect the attacks with an F1-score of 0.000, while the Gating Hybrid RF+IF achieves an F1-score of 0.405, which corresponds to a 40.5 percentage-point lift from the F1-score of the anomaly component IF. Thirdly, the Gating Hybrid RF+IF achieves an SQL Injection recall of 47.6% on the zero-day test scenario, while the standalone RF and GBT classifiers achieve an SQL Injection recall of 33.3% on average. All the abovementioned results are supported by 95% Wilson confidence intervals, and we provide root-cause analysis for the extreme results.
Authors - Moises Toapanta T, Jeanette Jordan Buenano, Nancy Jordan Buenano, Maria Cristina Espin Melendez, Pamela Toapanta Pavon, Rocio Llumiquinga A., Dafna Guaman B., Eriannys Gomez D., Pedro Echeverria B. Abstract - The globalization of Information and Communications Technologies (ICTs) the Internet, artificial intelligence (AI), and social media poses serious threats to the integrity, confidentiality, and authenticity of information in higher education institutions (HEIs). The central problem lies in the absence of robust legal frameworks regulating the use of AI, particularly in relation to personal data protection. This study examines perspectives on AI and social media, together with the legal foundations required for the effective administration of HEIs. Using the deductive method and exploratory research, key actors in institutional governance were identified, administrative strengthening indicators were de-fined, and an integrated model was developed to link AI, social media, and regulatory frameworks. It is concluded that improving institutional governance re-quires mitigating the risks associated with the use of these technologies through legal frameworks aligned with national constitutions and regulations. Ecuador, like most Latin American countries, currently lacks such legislation and remains in the analysis phase.
