Sentiment analysis is increasingly pivotal in natural language processing (NLP), crucial for deciphering public opinions across diverse sectors. This research conducts a comparative examination of rule-based and machine learning (ML) methods in sentiment analysis, specifically targeting the Kazakh language. Given the Kazakh language's limited exposure in computational linguistics, the study meticulously evaluates datasets from news articles, literature, and Amazon product reviews, aiming to compare the efficiency, adaptability, and overall performance of these distinct approaches. Employing a detailed set of evaluation metrics such as accuracy, precision, recall, and computational efficiency, the study provides a comprehensive analysis of the strengths and limitations of rule-based techniques versus ML models like Logistic Regression, Multinomial Naive Bayes, Decision Trees, Random Forest, and XGBoost. The findings suggest rule-based methods excel in identifying nuanced emotional expressions within literary texts, while ML models demonstrate superior adaptability and robustness, particularly effective in handling the linguistic variations found in news and reviews. Despite the strengths identified, the study also reveals significant limitations of the rule-based approach, especially in broader contexts beyond literary analysis. This highlights an imperative for future research to integrate sentiment dictionaries or domain-specific lexicons that cater to a wider array of linguistic styles, potentially enhancing sentiment analysis tools' applicability in Kazakh and similar less-studied languages. This investigation contributes significantly to the sentiment analysis discourse, offering invaluable insights for both researchers and practitioners by elucidating the complexities of applying NLP technologies across diverse linguistic landscapes, thus advancing the understanding and methodologies of sentiment analysis in the Kazakh language context.

Distributed Systems are widely used in industrial projects and scientific research. The Apache Hadoop environment, which works on the MapReduce paradigm, lost popularity because new, modern tools were developed. For example, Apache Spark is preferred in some cases since it uses RAM resources to hold intermediate calculations; therefore, it works faster and is easier to use. In order to take full advantage of it, users must think about the MapReduce concept. In this paper, a usual solution and MapReduce solution of ten problems were compared by their pseudocodes and categorized into five groups. According to these groups’ descriptions and pseudocodes, readers can get a concept of MapReduce without taking specific courses. This paper proposes a five-category classification methodology to help distributed-system users learn the MapReduce paradigm fast. The proposed methodology is illustrated with ten tasks. Furthermore, statistical analysis is carried out to test if the proposed classification methodology affects learner performance. The results of this study indicate that the proposed model outperforms the traditional approach with statistical significance, as evidenced by a p-value of less than 0.05. The policy implication is that educational institutions and organizations could adopt the proposed classification methodology to help learners and employees acquire the necessary knowledge and skills to use distributed systems effectively.

Esophageal cancer (EC) is a growing public health concern in Ethiopia, particularly in the Arsi Zone, Oromia. While numerous studies have identified key dietary and environmental risk factors contributing to EC, the complexity of scientific data often limits its accessibility to healthcare professionals, policy-makers, and the general public. This study emphasizes the power of data visualization in transforming epidemiological findings into intuitive, user-friendly formats that enhance understanding, support decision-making, and promote targeted interventions. By employing various visualization techniques, including grouped bar charts, heatmaps, stacked bar charts, and forest plots, this study effectively illustrates the distribution of cases and controls, highlights major risk factors, and reveals key trends in esophageal cancer prevalence. Additionally, an interactive dashboard was developed to facilitate real-time data exploration, allowing healthcare professionals and researchers to uncover patterns that aid in risk assessment and disease prevention. The findings demonstrate that data visualization serves as a critical tool in bridging the gap between complex statistical analysis and practical health applications. By making esophageal cancer risk factors more comprehensible, visualization enhances public awareness, informs clinical decision-making, and supports policymakers in designing effective preventive strategies. This study underscores the need for integrating data visualization into public health research and calls for future advancements in interactive and predictive tools to further improve cancer prevention efforts in Ethiopia and beyond.

This paper presents the development of a question-answering system that can assist university students with academic and administrative questions. We present a new approach that examines various chunking approaches to the Retrieval-Augmented Generation process. Although RAG is typically used with standard chunking methods, this paper presents row-based chunking, tailored to structured question-answer datasets, in order to enhance context retrieval for large language models. To establish its effectiveness, we conducted a human evaluation to compare the outputs it generated with those generated using standard and row-based chunking. The individuals who tested our system were both students and educators at the university. We concluded that row-based chunking gives more coherent and relevant contextual data than standard ways of chunking when applied to structured data sets. This work highlights the potential of using chunking methods to improve RAG-based systems for domain-specific applications, paving the way towards more accurate and context-sensitive AI-based aid in educational settings.