Riga Aeronautical Institute

This study critically examines the evolution and effectiveness of Fleet Air International(FAI), a commercial airline operator’s Management System within the context of contemporaryaviation safety regulation and operational demands. Emphasizing empiricaldata collected from internal event reports in January 2025, this research analyzessafety event trends, the management of organizational changes, audit outcomes, andcorrective action efficacy. The work situates FAI’s system against regulatory frameworksincluding (EU) Regulation 965/2012 and Regulation 1321/2014. It also providesevidence-based insights into risk assessment and mitigation strategies. The studyconcludes with recommendations to enhance safety performance indicators and proceduralcompliance, contributing to the broader field of aviation safety management.

Abstract— Breast cancer remains one of the leading causes of mortality among women worldwide, making early and accurate diagnosis crucial for improving patient outcomes. Traditional machine learning models often struggle to maintain high classification accuracy due to data variability and the complex nature of tumor characteristics. This study proposes an adaptive voting ensemble learning algorithm to enhance breast cancer classification performance. The ensemble integrates multiple classifiers—such as Decision Trees, Support Vector Machines, and K-Nearest Neighbors—by assigning dynamic weights based on each model's real-time performance. The algorithm is evaluated using the Wisconsin Breast Cancer Dataset (WBCD), and its effectiveness is measured against individual classifiers using metrics like accuracy, precision, recall, and F1-score. Results demonstrate that the adaptive ensemble significantly outperforms standalone models, offering a more robust and reliable approach for breast cancer prediction. This method shows promise for application in clinical decision support systems, contributing to more accurate diagnostics and better treatment planning. Keywords- Breast Cancer Classification, Adaptive Voting, Ensemble Learning, Machine Learning, Wisconsin Breast Cancer Dataset (WBCD), Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Medical Diagnosis, Predictive Modeling

Colorectal cancer (CRC) continues to be a significant global health issue as a result of the difficulties associated with the early and precise detection of lesions through colonoscopy. We propose CADxPolyDetect, a clinically explainable hybrid deep learning system for multi-class colorectal lesion detection using augmented colonoscopy images, to address diagnostic limitations that arise from class imbalance, interpretability gaps, and spatial complexity in colonoscopic images. A five-stage experimental pipeline is integrated into the framework. Initially, the original Hyper Kvasir dataset (10,672 images) is augmented to 23,000 images using Deep Convolutional Generative Adversarial Networks (DCGAN) and class balanced using SMOTE. Three pre-trained CNNs—ResNet-50, DenseNet-201, and VGG-16—are implemented in Stage 2 to execute end-to-end feature extraction. Transformer networks are integrated with these CNNs in Stage 3 to capture global spatial dependencies. Stage 4 incorporates multi-class support vector machines (SVM) for the final classification of 23 colorectal conditions. Stage 5 improves interpretability by utilising Grad-CAM to produce clinically relevant heatmaps that localise malignant polyps and facilitate transparent decision-making. The DenseNet-201 + Transformer + Multi-Class SVM model showed the best results of all the setups tested, achieving an overall accuracy of 98%, an F1-score of 0.98, a precision of 97%, and a recall of 99%, based on a 70:30 split of training and testing data. Robustness, precision, and clinical trust are guaranteed through the integration of attention-based visualisations, deep ensemble learning, and augmented data. Therefore, CADxPolyDetect sets a new benchmark in AI-supported colonoscopy by offering a decision support system that is easy to understand and can grow to meet needs for the early detection and screening of colorectal cancer.

Financial inclusion is essential for fostering economic growth and reducing poverty, yet many individuals and businesses in emerging economies face barriers to accessing formal financial services. This paper investigates strategies aimed at bridging the gap in financial inclusion within emerging markets. Through a comprehensive analysis of primary data, this study explores innovative approaches to overcome these challenges. Engaging stakeholders including employees and students in banks, we identify effective strategies to reach underserved communities. By examining transaction patterns, customer feedback, and market dynamics, we develop tailored solutions to address specific barriers and promote inclusive finance. This research contributes to the advancement of knowledge in enhancing financial inclusion in emerging markets, ultimately fostering inclusive economic development. Financial inclusion remains a pressing issue in emerging markets, with millions of individuals lacking access to basic banking services. It explores various strategies aimed at bridging the gap and extending financial services to the unbanked and underbanked populations. Drawing on insights from the literature review the paper examines the role of digital financial services. By synthesizing these strategies, policymakers, financial institutions, and development practitioners can devise comprehensive and tailored interventions to promote financial inclusion, thereby fostering inclusive economic growth and poverty alleviation in emerging markets.

Social media's pervasive presence in the digital economy demands innovative business models for strategic marketing success. With half of the global population active on social platforms, organizations can leverage these channels for market insights and enhanced business intelligence. Utilizing social media allows companies to craft flexible marketing strategies, fostering engagement, communication, and performance monitoring. Despite its potential, research on social media strategy remains fragmented, impeding its advancement as a robust area of study. Conducting a literature review can help consolidate theoretical and empirical developments while pinpointing key research themes.