Turkish Journal of Computer and Mathematics Education (TURCOMAT)

A COMPREHENSIVE SURVEY OF MEMORY UPDATE MECHANISMS FOR CONTINUAL LEARNING ON TEXT DATASETS

2025-02-05T07:11:57-05:00

Over the last several years, there has been a growing focus on the CL field in the context of machine learning and its goal to create models capable of learning new tasks step by step without loss of prior knowledge. Among these, catastrophic forgetting is especially challenging in real-world settings where the data experience changes over time. To this effect, what has become pivotal for models is mechanisms for memory update to enable the models to learn information as well as update what has been previously learned easily. This survey specifically investigates the memory update strategy in the continual learning setup wherein new categories and domains are continuously added in the text datasets including sentiment analysis, named entity recognition, text classification tasks etc. Moving on, three primary memory update strategies of memory replay, memory consolidation, and parameter isolation are discussed; this paper further addresses certain adaptations of the proposed methods for text-based applications. Memory replay means that part of previous data is stored to be replayed when new tasks are learned while memory consolidation strengthens only significant memories. Parameter isolation helps avoid masking previous tasks or overwriting the parameters when the machine learning algorithm is trained to accomplish new tasks. In this paper, we discuss the latest in these techniques and offer a thorough insight into their use in text datasets such as Amazon Reviews and Yelp Reviews. Further, we outline the primary drawbacks of existing solutions for memory updates such as capacity limitations, domain variation, and continually learning without having access to new task information. In addition, a summary table of literature review identifying the most relevant works within the field is offered. Lastly, we discuss the remaining issues and potential research directions where more focus and development should be given in CL for text data by noting the importance of efficient and adaptive update policies towards the memory.

Investigating the behavior of dynamic systems based on linear differential equations

2025-04-18T10:49:34-04:00

Linear differential equations are fundamental tools in the analysis and modeling of dynamical systems that are used in many physical and engineering phenomena. This paper examines the structure, properties, and applications of these linear equations in the analysis of mass-spring systems, RLC circuits, and heat transfer processes. By presenting mathematical models and analyzing the time responses of these systems, it is shown how linear equations can model complex behaviors in a simple, predictable, and robust manner. The results of this research show that linear differential equations, despite their simplicity, are effective tools for the analysis of physical systems, and are used to more accurately model more complex systems over time.

Design and Implementation of a Cost-Effective, Network-Based Clinic Management System for Small Healthcare Facilities in Iraq

2025-04-10T10:53:25-04:00

Managing clinics efficiently requires a cost-effective and user-friendly system that simplifies daily operations while maintaining data security and accessibility. This paper proposes a clinic management system that does not require a dedicated server in order to minimize costs and ease the process of setup. The system is developed using SQLite, a lightweight database management system that enables easy data management on local machines and supports networked terminals. For the best performance, we divide the data into two SQLite files; the first for the basic activities of the clinic and the second for prescriptions and other attachments to enhance the processing and use of the system. The system is developed using C# and has a friendly graphical user interface for doctors and secretaries. It has role-based access control that provides secure user authentication and database is encryption to ensure that patient’s information is well protected. The system is deployed via Windows network sharing, which makes it very easy to connect multiple terminals simultaneously through LAN or Wi-Fi without the need of professional IT personnel. This system has been used in more than 50 clinics and has proven to be reliable, efficient and easy to use and is therefore suitable for small to medium size healthcare institutions. It improves the management of the patient’s records, the flow of work, and provides a cost-effective alternative to traditional clinic management solutions.

BUILDING A DYNAMIC CROPPING TREND VISUALIZATION SOFTWARE TO ASSESS FARMING PROGRESSIVENESS

2025-01-06T04:47:57-05:00

This thesis presents a comprehensive study that unfolds in two key dimensions. Firstly, a novel cropping trend visualization software was developed, aiming to provide a dynamic and insightful tool for assessing farming progressiveness. This software, a product of meticulous design and implementation, serves as an innovative instrument for visualizing and analyzing cropping trends in agriculture. In the second part of the study, the software was utilized to gather descriptive and inferential statistical data from a sample of 111 farmers. The data analysis reveals noteworthy correlations between specific agricultural practices and farming progressiveness. Key findings indicate significant positive associations between technology adoption, climate adaptation, crop variety diversity, and environmentally sustainable practices with higher levels of farming progressiveness. While certain trends exhibit statistical significance, caution is advised in drawing robust conclusions for factors with limited statistical support. These empirical insights provide a valuable foundation for policymakers, agricultural extension services, and stakeholders to strategically promote practices that enhance the overall progressiveness of farmers. The dual contribution of the developed software and the derived insights from its application underscores the significance of integrating technology in advancing sustainable and progressive agricultural practices.

Numerical method for simulating the model of using insecticide for the optimal control of mosquitoes for the eradication of malaria

2024-12-30T08:02:07-05:00

In order to simulate the mathematical model of eliminating malaria by controlling the population of
mosquitoes with insecticide and the insecticide's residual effects, the study developed a four points hybrid block
algorithm. The convergence and stability qualities of the block method are established. The block approach is
applied after the variable control problems are generated using Pontryagin's principle. The forward-backward sweep
methods of the block method are applied. The method is then implemented using a computer code using MATLAB
R2018a mathematical software. According to the findings of this study, the simulated result from this approach
displayed a significantly lower number of mosquitoes while lessening the negative effects of the insecticide, which
in turn will reduce the high rate of malaria spreading.

Mathematical Modelling and Argumentation: Designing a Task to Strengthen Variational Thinking by Integrating Data Science

2025-01-16T10:05:14-05:00

This study explores the significance of task design in fostering variational thinking through the principles of PyLVar and task design in mathematics education, particularly in the incorporation of technological and scientific tools to enhance modelling and argumentation skills among secondary school students. To develop and implement interactive mathematical tasks based on the PyLVar principle and task design, incorporating data science tools and STEAM. A qualitative-descriptive approach was employed to design, implement, and analyse interactive tasks centred on the principles of variational thinking, argumentation, and modelling. The sample consisted of nine participants selected from a group of 11th-grade students at a state school, chosen for convenience. Data were gathered through experimental activities, processed using technological tools, and analysed with variational strategies. Students demonstrated a marked improvement in their abilities in mathematical modelling and argumentation. The tasks designed, structured around the PyLVar principles, enabled the identification of variation patterns and the development of critical and analytical skills. Contextualised interactive mathematical tasks not only bolster variational thinking but also equip students to address real-world problems by connecting abstract concepts to practical applications and encouraging the integration of technological tools and interdisciplinary approaches.

AI-Powered Encryption Revolutionizing Cybersecurity with Adaptive Cryptographic Algorithms

2025-01-18T06:21:52-05:00

This paper proposes a state-of-the-art encryption technique that integrates artificial intelligence into a dynamic, content-aware security system. We introduce an AI-powered encryption framework that automatically adjusts cryptographic parameters based on message sensitivity, effectively balancing security requirements and computational efficiency. The system combines a DistilBERT-based neural network for real-time content sensitivity analysis with a flexible encryption mechanism that adapts key lengths, iteration counts, and entropy levels on the fly.

Our implementation demonstrates significant adaptability, with a correlation of 0.974 between content sensitivity and security parameters. The system distinguishes between different security requirements, using 32-byte keys with millions of iteration rounds for high-sensitivity content (sensitivity score of 8.64) and 16-byte keys with reduced iterations for low-sensitivity messages (sensitivity score of 3.10). The processing time scales linearly with security requirements, ranging from 300 ms for low-sensitivity content to 732 ms for high-security encryption.

Performance evaluation was highly effective, with the system achieving an overall score of 8.64/10, including 9.87/10 for adaptability and 9.12/10 for performance efficiency. The security level rated high at 7.37/10 while maintaining manageable computational overhead. The framework effectively handled different types of content without sacrificing encryption-decryption accuracy across all levels of sensitivity.

This work signifies a significant leap forward in the field of adaptive cryptography, demonstrating the capabilities of AI-driven security systems that can automate and optimize encryption parameters without compromising security standards. These results suggest that this approach may well represent the future of encrypted communications, providing scaled security appropriately without human intervention.

Practical Applications of Homogeneous Coordinates in Image Transformations Using MATLAB

2025-03-07T06:36:14-05:00

Homogeneous coordinates offer a robust mathematical framework for representing and executing geometric transformations in image processing, computer vision, robotics, and computer graphics. By embedding Euclidean space into a higher-dimensional projective space, they provide a unified mechanism for handling affine transformations, such as translation, rotation, scaling, and shear, as well as projective transformations like perspective projection. This study explores the practical applications of homogeneous coordinates within the MATLAB environment, leveraging its matrix manipulation capabilities to implement these transformations efficiently. Homogeneous coordinates simplify complex transformation pipelines through matrix concatenation, enabling seamless execution of combined operations while preserving computational efficiency and accuracy. Key applications demonstrated include image registration, warping, rectification, 3D modeling, and camera calibration, emphasizing their critical role in medical imaging, virtual reality, and augmented reality. MATLAB's intuitive programming environment and advanced visualization tools further enhance the accessibility and applicability of these techniques. This article provides detailed explanations, MATLAB code implementations, and visual demonstrations to bridge the gap between theoretical foundations and real-world applications, making it an invaluable resource for researchers, practitioners, and students in the fields of image processing and computer vision.