Turkish Journal of Computer and Mathematics Education (TURCOMAT)
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HOLLOWNESS OF CIVILIZED SOCIETY IN THE NOVEL THE APPRENTICE
No full textIn the novel The Apprentice, the depiction of a civilised society serves as a critical backdrop against which the protagonist’s journey unfolds. The protagonist enters a world where rules, order, and societal norms dictate behaviour and interactions. This civilised society starkly contrasts the protagonist’s upbringing and challenges the protagonist’s beliefs and values. The protagonist confronts societal expectations in a novel that examines the complexities of a civilised society, questioning traditional norms and values. This paper investigates how the author portrays this society and its key traits, reflecting or contrasting with real-world societies to reveal underlying critiques. A central theme of hollowness permeates the narrative, highlighting the emptiness within the characters and their environment. Through a detailed analysis of key events and moments, the paper aims to uncover deeper meanings and the commentary on human nature within a structured society
Autonomous Road Damage Detection using Unmanned Aerial Vehicle Images and YOLO V8 Methods
No full textUsing photos from Unmanned Aerial Vehicles (UAVs) and deep learning algorithms, this research provides a revolutionary automated road damage identification method. In order to provide a secure and long-lasting transportation system, road infrastructure maintenance is essential. On the other hand, gathering road damage data by hand may be dangerous and labor-intensive. Therefore, we suggest using artificial intelligence (AI) and unmanned aerial vehicles (UAVs) to greatly increase the effectiveness and precision of road damage identification. For object recognition and localisation in UAV photos, our suggested method makes use of three algorithms: YOLOv4, YOLOv5, and YOLOv7. We used a mix of a Spanish roadway dataset and the Chinese RDD2022 dataset for training and testing these methods. Our method obtains 59.9% average precision ([email protected]) for the YOLOv5 versions, 65.70% [email protected] when using the YOLOv5 version using the Transformers Prediction the Head, or 73.20% [email protected] for that YOLOv7 version, testing results show the effectiveness of our methodology. These findings open the door for further study in this area and show the possibilities of employing deep learning and UAVs for automatic road damage identification
A FORENSICS ACTIVITY LOGGER TO EXTRACT USER ACTIVITY FROM MOBILE DEVICES
No full textMobile devices have become one of the most often used tools in everyday life, mostly because of the importance of its apps. In this case, mobile devices become personal trackers for daily activities that provide important information about the user by recording extra data in addition to the user\u27s personal information. As a consequence of this information gathering, several tools are now accessible for use on mobile devices, however each tool is only able to provide discrete details about a certain application or activity. Consequently, the present research proposes a technology that allows investigators to get a detailed report and time line of all operations performed on the device. This report combines data from several sources to generate a unique collection of facts. Furthermore, an example is provided to illustrate how the solution works, highlighting the practicality of the instrument as well as the way in which investigators need to use it
Analysis of the computerized activities included in Palestinian Mathematics textbooks
No full textPurpose– The study aimed to analyze the standards of computerized activities included in mathematics textbooks based on international standards and indicators.
Design/methodology/approach–The researcher developed a tool (content analysis) to analyze these activities and verified its validity and reliability. This tool was then applied to a sample of mathematics textbooks.
Findings –The results revealed a significant gap between the expected standards and indicators and the actual standards and indicators found in the mathematics textbooks.
Originality/value – It is crucial to consider fundamental international standards in utilizing computer software in mathematics textbooks, including the design of mathematical tasks, posing mathematical questions, facilitating and simplifying mathematical discussions, and assessing student learning. Based on these findings, the researcher recommended a reassessment of the development of computerized activities in mathematics textbooks
A study about mathematical analysis of Hepatitis B virus using Optimal Control approach
No full textThis research focuses on the mathematical modeling of Hepatitis B virus (HBV) dynamics using optimal control theory to enhance understanding of transmission patterns and optimize intervention strategies. An ordinary differential equation (ODE) model is proposed, capturing the dynamics of HBV transmission through distinct compartments: susceptible, exposed, infected, liver cirrhosis, and removed. Advanced liver cirrhosis, a severe stage of chronic liver disease caused by sustained and progressive damage, has emerged as a critical non-communicable health concern. The study employs mathematical simulations to analyze the impact of various control measures in mitigating HBV spread. Through the application of optimal control theory and the Hamiltonian principle, the research identifies effective strategies, such as vaccination, treatment, and awareness campaigns, to manage and limit HBV transmission. The primary objective is to minimize the number of individuals in the infected and cirrhotic stages while reducing associated intervention costs. By targeting HBV, a leading cause of cirrhosis, the study aims to lower the incidence of chronic liver disease. The findings highlight the importance of vaccination, effective treatment protocols, and public awareness in curbing the progression of HBV and reducing its long-term health impacts. This research provides crucial insights for public health policies and the development of targeted strategies to combat HBV and its complications.
 
Architecting Scalable LLM-Powered Employee Engagement Systems: A Multi-Modal Framework for Enterprise HRIS Integration and Longitudinal Efficacy Analysis
No full textThis article provides a comprehensive technique for incorporating Large Language Models (LLMs) into corporate employee engagement platforms, with an emphasis on technical design, implementation challenges, and longitudinal effect analysis. We examine sophisticated fine-tuning methods, such as bias mitigation strategies and privacy-preserving approaches, using proprietary HR datasets. The report emphasizes significant improvements in operational efficiency, with AI-powered HR solutions showing a 32% improvement in process optimization and 91.2% accuracy in employee feedback analysis across many languages. To address significant concerns about data privacy, scalability, and long-term efficacy, our system employs a multi-layered approach that incorporates federated learning implementations, differential privacy techniques, and robust security mechanisms. The implementation outcomes show notable benefits, including a 34% rise in employee satisfaction metrics and a 41% reduction in time-toinsight for HR analytics, while closely conforming to GDPR and CCPA laws
Federated Cloud Approaches for Multi-Regional Payment Messaging Systems
No full textPayment messaging systems are becoming an essen- tial element for many cross-border financial processes. However, supporting the growing volume of payment messages while adhering to local data residency policies requires significant investments, which can be a barrier for many regional players. Federated cloud approaches—data-sharing partnerships with cross-border regions that reciprocate the processing of mes- sages—could help multi-regional cloud providers offer such services in a cost-effective, secure, and compliant manner. The candidate federated architecture models are examined from key aspects of multi-regional message-processing and offering- resilience perspectives. These aspects include the support of local data residency; treaty-based interoperability for data-sharing under local sovereign laws; a reduced attack surface; coverage of service-messaging supply chains; and support of incoming financial borders where Director Exposure and common mes- saging protocol. By enabling low-latency, cost-efficient legal- standardized cost-based reciprocal payment messaging; with copy-matching support; and for fully managed, self-service ser- vices
Performance Analysis of Big Data with Data models using Artificial Intelligence
No full textProliferation of new information sources such as medical images, financial data, sales data, radio frequency identification and web tracking data, there is a challenge to decipher trends and gain sense of data that is orders of magnitude larger than ever earlier. One of the technologies most often associated with the era of big data is Hadoop. Although in that respect is much expert information about Hadoop, there is not much info around how to effectively structure data in a Hadoop environment. Though the nature of parallel processing and the MapReduce system provide an optimal environment for processing big data quickly, the structure of the big data itself plays a vital role. This paper explores doable used for data modeling in a Hadoop environment. Specifically, the purpose of the experiments described in this paper was to figure out the best structure and physical modeling techniques for storing data in a Hadoop cluster using Hive to enable efficient data access. Although other software interacts with Hadoop, the experiments focused on Hive. The Hive infrastructure is most felicitous for traditional data warehousing-type applications. The experiment does not focus on HBase. This paper explores a data partition strategy and investigates the role indexing, data types, file types, and other data architecture decisions play in designing data structures in Hive. To test the different data structures, it focused on typical queries utilized for analyzing web traffic data. These test included most referring sites, web analyses such as counts of visitors, and other typical business questions used by weblog data. The primary measure for culling the optimal structure of data in the Hive is predicated on the performance of web analysis queries. For comparison purposes, it was quantified the performance in Hive and the performance in an RDBMS. The reason for this comparison is to more preponderant understand how the techniques that we are habituated with utilizing in an RDBMS work in the Hive environment. It explored techniques such as storing data as a compressed sequence file in Hive that are particular to the Hive architecture. Through these experiments, it endeavored to show that how data is structured (in effect, data modeling) is just as consequential in an immensely colossal data environment as it is in the traditional database world
A ROBUST DETECTION FRAUDULENT TRANSACTIONS IN BANKING USING MACHINE LEARNING
No full textVulnerability in banking systems has exposed us to fraudulent acts, which cause severe damage to both customers and the bank in terms of loss of money and reputation. Financial fraud in banks is estimated to result in a significant amount of financial loss annually. Early detection of this helps to mitigate the fraud, by developing a counter strategy and recovering from such losses. A machine learning-based approach is proposed in this paper to contribute to fraud detection successfully. The artificial intelligence (AI) based model will speed up the check verification to counteract the counterfeits and lower the damage. In this paper, we analyzed numerous intelligent algorithms trained on a public dataset to find the correlation of certain factors with fraudulence. The dataset utilized for this research is resampled to minimize the high class of imbalance in it and analyzed the data using the proposed algorithm for better accuracy
FORCASTING ACADMIC PERFORMANCE IN COMPUTER SCIENCE STUDENTS BASEDON FUTURE ANALYSIS METHOD
No full textThe ever increasing importance of education has drivenresearchers and educators to seek innovative methods forenhancing student performance and understanding the factorsthat contribute to academic success. This paper presents a methodology for predicting CGPA SGPA that leverages machine learning techniques to forecast students\u27academic achievements based on a variety of features, such asdemographic information, academic history, and behavioural patterns. The proposed students academic performance method utilizes a real-world collected dataset from multiple educational institutions toensure an accurate and comprehensive analysis. The proposed methodology starts with a data preparationstage, where the data is cleansed and organized for analysis. This process encompasses tasks such as handling missing values, scaling the data, and transforming variables ifnecessary. The feature analysis technique was used to select the most important features for the students academic performance model. A number ofmachine learning classifiers were tested, and the feature analysis was found to be the best performer. The results of this study demonstrate the potential of algorithms in predicting student performance andidentifying key factors that influence academic success. This information can be leveraged by educators and academicinstitutions to develop targeted intervention strategies, tailoredlearning experiences, and personalized recommendations forstudents, ultimately fostering a more effective learningenvironment and improving overall educational outcomes