Turkish Journal of Computer and Mathematics Education (TURCOMAT)

DETECTION OF FRAUDULENT PHONE CALLS DETECTION IN MOBILE APPLICATIONS

2024-05-20T05:36:15+00:00

The primary challenge faced over the course of this decade-long endeavour is the difficulty in devising effective features without direct access to telephony network infrastructure. we conducted an extensive three-month measurement study using these call logs, which encompassed a staggering 9 billion records. Based on the insights gleaned from this study, we identified and designed 29 features that could be used by machine learning algorithms to predict malicious calls. Fraudulent phone calls or scams and spam s via telephone or mobile phone have become a common threat to individuals and organizations. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools in detecting and analyzing fraud or malicious calls. This paper presents an overview of AI-based fraud or spam detection and analysis techniques, along with its challenges and potential solutions. The novel fraud call detection approach is proposed that achieved high accuracy and precision. The outcomes revealed that the most effective approach could reduce unblocked malicious calls by up to 90%, while maintaining a precision rate exceeding 93.79% for benign call traffic. Moreover, our analysis demonstrated that these models could be implemented efficiently without incurring significant latency overhead.

Spectrum Sensing Using Cooperative Matched Filter Detector in Cognitive Radio

2024-05-13T05:08:44+00:00

The vast rise in the number of internet-connected devices necessitates a more accessible spectrum. As a result, Cognitive Radio was already proposed as a solution to the problem of restricted spectrum resources by utilizing available spectrum which is assigned to primary users. This method allows the secondary user to utilize the spectrum whenever the primary user is not using it, and it does so without intruding with the primary user. Whenever the secondary user detects the spectrum, it faces many issues, such as complexity in sensing, leading to a lack of noise value, and the primary user is hidden to all secondary users. In order to tackle these challenges, many spectrum sensing frameworks were introduced in the literature. In this paper, an adaptive threshold matched filter detector and a cooperative matched filter detector frameworks are utilized to detect the spectrum and resolve the issues above. The probability of detection (Pd), probability of miss detection (Pm), and probability of false alarm (Pf) are the metrics used to assess sensing accuracy. To simulate suggested detectors results and proficiency, the MATLAB R2020a software was utilized. In comparison to earlier studies, the simulation conclusions reveal that the detection process starts with lower SNR values compared to previous work.

Reduction Jammer Detection and Recovery Algorithms for DSRC Safety Application in VANET

2024-05-13T05:25:49+00:00

Intelligent Transportation Systems (ITS) encompasses technologies, services, and applications facilitating communication between vehicles (V2V) and between vehicles and fixed infrastructure (V2I and I2V). This mutual interaction constitutes a Vehicular Ad-Hoc Network (VANET) which supports a plethora of applications targeting critical transportation aspects, such as safety, mobility, and environmental considerations. Dedicated Short Range Communications (DSRC), operating on the 5.9 GHz band, is pivotal for such exchanges. We introduced an innovative algorithm designed to identify jamming attacks and transition the Safety Application to a secure fail-safe mode. This algorithm leverages a dual-metric strategy, incorporating both distance and PDR measurements. Field tests confirm that our algorithm adeptly recognizes the activities of deceptive jammers, ensuring a prompt shift of the safety application into its fail-safe state. This paper delves into these countermeasures, evaluating their efficiency via mathematical modeling, simulations, and on-ground testing. Findings acknowledge that these strategies bolster the reliability of safety applications in jamming scenarios. Furthermore, the approaches propounded align with ongoing standardization endeavors by relevant authorities, ensuring communication mediums remain unhindered.

EFFECTS OF ETHNO-MATHEMATICS INSTRUCTIONAL APPROACH AND PROBLEM-BASED LEARNING STRATEGY ON STUDENTS’ INTEREST, ACHIEVEMENT AND RETENTION IN GEOMETRY IN BENUE STATE, NIGERIA

2024-06-06T11:22:17+00:00

This study examined the effects of the Ethno-mathematics instructional approach and Problem-based learning strategy on students' interest, achievement, and retention in geometry in Benue State, Nigeria. Quasi-experimental design involving pretest, posttest, and post-posttest with two experimental groups and one control group. Twelve research questions guided the study and twelve hypotheses were tested at 0.05 level of significance. The population comprised 20,213 Junior Secondary Two (JS II) students, with a sample of 1,200 students selected using simple random sampling. Two instruments were used for data collection namely; Geometry Interest Ratings Scale and Geometry Achievement Test. Content validity index for GIRS was 0.78 and construct validity for GAT was 0.88. The reliability of GIRS was tested using Cronbach Alpha formula which yielded an index of 0.81 and K-R21 was used to determine the reliability index of GAT which yielded an index of 0.75. Data were analyzed using mean and standard deviation for research questions and Analysis of Covariance (ANCOVA) for hypotheses The findings indicated a significant difference in the mean interest ratings of students taught geometry using the ethno-mathematics instructional approach, problem-based learning strategy, and conventional teaching approach. However, no significant difference was found in the mean interest scores between male and female students taught geometry using the ethnomathematics instructional approach or problem-based learning strategy. Additionally, there was a significant difference in the mean achievement and retention scores of students taught geometry using these approaches compared to the conventional teaching approach. No significant difference was found in the mean achievement or retention scores between male and female students taught using these approaches. The ANCOVA result on the interaction effect between methods and gender on retention indicates that there is no significant interaction effect between Ethno-mathematics instructional approach, Problem-based learning strategy and gender on retention. Based on the findings of this study, it is recommended that: Students should be subjected to consistent utilisation of ethno-mathematical operations within their culture, adoption of the ethno-mathematics instructional approach in the school system, and training of mathematics teachers in the use of ethno-mathematics instructional approach to improve students' interest, achievement and retention in geometry.

Computing the Plankton-Oxygen Dynamics Model Using Deep Neural Networks in the Context of Climate Change

2024-03-28T10:46:40+00:00

This study proposes a novel approach to computing the plankton-oxygen dynamics model using deep neural networks (DNNs) within the context of climate change. By leveraging advanced computational methods, particularly deep learning algorithms, we aim to enhance our understanding of how plankton populations and oxygen concentrations interact in response to changing environmental conditions. The integration of DNNs offers several advantages, including the ability to capture complex nonlinear relationships and patterns from large datasets, making them well-suited for modeling dynamic systems such as aquatic ecosystems. By training DNNs on observational data and environmental variables, we can develop predictive models that simulate the behavior of plankton-oxygen dynamics under different climate scenarios. This research builds upon existing studies in ecological modeling and deep learning techniques to advance our knowledge of plankton-oxygen dynamics and their implications for ecosystem resilience in the face of climate change. By computationally modeling these dynamics, we can gain valuable insights into the mechanisms driving ecosystem responses to environmental stressors and inform conservation efforts and policy decisions.

Impact of Climate Change on Arctic Fox Population Dynamics: A Mathematical Modeling Approach

2024-03-28T10:58:55+00:00

This study focuses on the impact of climate change on Arctic fox populations using mathematical modeling. The research employs a basic Lotka-Volterra-style model to simulate the effects of temperature, precipitation, and snow cover on the Arctic fox population dynamics. The model is based on the assumption that the population growth rate is limited by the carrying capacity of the environment and is influenced by these environmental factors. The study provides insights into the complex relationship between environmental factors and population changes, highlighting the need for more sophisticated models to holistically understand the impact of climate change on ecosystems. The findings underscore the importance of mathematical models in guiding adaptive strategies for ecosystem management amidst changing climates, emphasizing the necessity for further research to comprehensively address climate-induced challenges and ensure a sustainable future for ecosystems and species.

Chemical Perspectives on the Impact of Climate Change in Afghanistan: A Comprehensive Review

2024-05-23T08:15:21+00:00

: Climate change is a global phenomenon that has significant impacts on various aspects of human life, including the environment, economy, and social well-being. Afghanistan, one of the least developed and most vulnerable countries to climate change, is facing alarming effects due to its high dependence on agricultural livelihoods, fragile environment, poor socio-economic development, high frequency of natural hazards, and over four decades of conflict. This comprehensive review aims to provide an overview of the current state of knowledge on the impact of climate change on Afghanistan's environment, economy, and society, and to highlight the vulnerability of Afghanistan to climate change. The review also explores the chemical composition of air pollutants in Afghan cities, the impact of air and water pollution on human health and the environment, the influence of climate change on soil composition and nutrient availability, and the implications for water resources, including groundwater quality and availability. Finally, the review discusses the chemical aspects of climate change adaptation and mitigation efforts in Afghanistan, focusing on innovative technologies and practices to address climate-related challenges in the country.