ADBA Computer Science
type: channel
description: ADBA Computer Science is a peer-reviewed interdisciplinary journal dedicated to advancing understanding and application of all areas within computer science. The journal serves as a nexus for researchers, educators, engineers, and practitioners to disseminate cutting-edge research findings, innovative theories, and practical applications across diverse computer science disciplines. The journal aims to foster interdisciplinary collaboration and facilitate the exchange of ideas among researchers and practitioners working in various fields of computer science. We welcome contributions that explore theoretical developments, computational methods, experimental observations, and practical implementations related to computer science. We are particularly interested in contributions that address the following topics: - Algorithms and Data Structures - Machine Learning and Artificial Intelligence - Software Engineering and Development - Database Management and Data Mining - Computer Graphics and Visualization - Cybersecurity and Information Assurance - Networking and Communications - Computational Biology and Bioinformatics - Human-Computer Interaction - Quantum Computing ADBA Computer Science strives to bridge the gap between theoretical computer science and practical technological advancements, contributing to the global discourse in technology and its applications. We encourage submissions that push forward the boundaries of our knowledge and contribute to the ongoing discussions in the computer science community. Through rigorous peer review and a commitment to quality, ADBA Computer Science seeks to uphold the highest standards of integrity and scholarly excellence. We invite authors to contribute their best work and join us in advancing the fields of computer science and technology.
publisher: adbascientific
language: en
publication name: ADBA Computer Science
issn: 3023-8544
copyright: The following copyright agreement is read and approved by authors before they publish their work to our journal's online system. The copyright notice text is regarded to have been approved by them after they give their approval. Copyright Notice Text Author(s); a) The submitted article is the original work of the author(s), and they do not plagiarize, b) All authors have participated in this study individually, and they take all responsibility for the study. c) All authors have seen and approved the final version of the submitted article, d) The article has not been published elsewhere or submitted for publication, e) They accept and undertake that the text, figures, and documents in the article do not violate the copyrights of other parties. The authors are deemed to have accepted the following terms by submitting their work to the journal under the CC BY-NC license. The copyright of the published article belongs to its author under CC BY-NC 4.0 license. The authors authorize the journal to publish the article and identify itself as the original publisher. The authors grant the right to freely use the article to any third party as long as the original authors and citation details are identified. Nothing in this license impairs or restricts an author's right to preserve the integrity and ownership of their work. All commercial rights related to the article belong to the authors. The authors own the copyright of their work published in ACS, and their work is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license allows the work to be shared, copied, and reproduced in any size and format except for commercial use. It also allows it to be rearranged, transformed, and adapted, including building upon the original work, with proper reference to it. CC BY-NC 4.0: https://creativecommons.org/licenses/by-nc/4.0/ The license owner cannot cancel these r…
items: Predictive Modeling for Milk Quality Using Machine Learning and XAI Algorithms
items: Chaos Analysis of a Fuel-Slosh Coupled Aircraft System
items: Stochastic Weather Simulation of Türkiye’s Geographical Regions: A Monte Carlo Framework
items: A Systematic Benchmark of Advanced Architectures for Automated Multi-Class Classification in Digital Pathology
items: Determination of Optimal Linear Congruence Generators Parameters with Heuristic Optimization Algorithms
items: Design of an ESP32-Based Smart Meteorological Data Collection Station for Renewable Energy Applications
items: Hyperparameter Optimization for Big Data: Adapting Sampling Methods to Apache Spark MLlib
items: Dynamics, Stability, and Bifurcation in Discrete-Time Predator-Prey Model
items: Microcontroller Execution, Random Number Generation and Chaos Annihilation in Piecewise Quadratic Map
items: A Mobile App for Enhancing Suture Skills through XAI
items: True Random Number Generator Design with A Fractional Order Sprott B Chaotic System
items: Chaos Annihilation-Based on Genetic Algorithms in a Non-smooth Air-gap Permanent Magnet Synchronous Motor Embedded in the Microcontroller
items: Computational Study of the Effect of Aluminum Content on the Thermodynamic Properties and Phase Stabilityof Mg-Al-Zn Alloys
items: Demystifying English Towns Educational Outcomes with Explainable Artificial Intelligence
items: Bibliometric Analysis of Studies on Cyber Crimes Between 2000-2023
items: High-Accuracy Prediction of Mechanical Properties of Ni-Cr-Fe Alloys Using Machine Learning
items: The Role of Technological Approaches in Cyber Security of Autonomous Vehicles
items: Evaluating the Effectiveness of Machine Learning Models in Predicting Student Academic Achievement
items: Deep Learning in Agriculture: Detection and Analysis of Sugar Beets with YOLOv8
items: Optimizing Diabetes Prediction: Addressing Data Imbalance with Machine Learning Algorithms
items: Enhancing Anomaly Detection in Large-Scale Log Data Using Machine Learning: A Comparative Study of SVM and KNN Algorithms with HDFS Dataset
items: Environmental Sustainability through AI: A Case Study on CO2 Emission Prediction

Predictive Modeling for Milk Quality Using Machine Learning and XAI Algorithms
type: item
description: Milk quality assessment is of critical importance for food safety and public health. Traditional milk quality evaluation relies on physicochemical measurements that require expert interpretation and may not directly support rapid or large-scale decision-making, increasing the need for data-driven and automated assessment methods. Although machine learning-based approaches have been widely applied in milk quality classification in recent years, the lack of transparency in model decision mechanisms and insufficient reporting of data preprocessing and data leakage control procedures pose significant limitations in terms of reliability. In this study, the milk quality classification performance of various supervised machine learning algorithms is comparatively evaluated using an open-access milk dataset. Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Artificial Neural Network (ANN) models are assessed under fair and consistent experimental conditions. The main contribution of this study lies in the application of group-based data partitioning strategies to prevent data leakage, rather than directly removing duplicate or highly similar records from the dataset. This approach prevents data loss and enables a more realistic evaluation of model performance. Furthermore, a targeted and minimalist preprocessing strategy is adopted by applying scaling exclusively to continuous variables. For hyperparameter optimization, Grid Search and Particle Swarm Optimization (PSO) methods are employed; notably, tree-based models optimized using PSO demonstrate more consistent classification performance. To move beyond predictive accuracy, Explainable Artificial Intelligence (XAI) approaches are utilized to enhance the interpretability of model decisions. In this context, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) methods are appli…
subject: Articles
  topic: section
subject: XAI
  topic: keywords
subject: Optimization algorithms
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Milk quality
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: Zeynep Çetinkaya
creator: Fahrettin Horasan
creator: Hüseyin Aydilek
creator: Mustafa Yasin Erten
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 63
ending page: 71
doi: 10.69882/adba.cs.2026019

Chaos Analysis of a Fuel-Slosh Coupled Aircraft System
type: item
description: This study investigates nonlinear and chaotic dynamics induced by fuel sloshing in a partially filled aircraft tank. The system is modeled as a rigid aircraft pitch degree of freedom coupled with a nonlinear slosh pendulum, representing the dominant liquid motion. Numerical simulations are used to analyze the system response through time series and phase portraits, revealing complex dynamical behavior. Chaotic dynamics are quantitatively confirmed using Lyapunov exponents and their parametric variation. In addition, bifurcation analyses are performed with respect to key system parameters, illustrating transitions from periodic to chaotic motion. The results highlight the significant impact of fuel slosh on aircraft pitch dynamics and emphasize the importance of accounting for nonlinear fluid–structure interactions in stability analysis and control-oriented modeling.
subject: Articles
  topic: section
subject: Nonlinear dynamics
  topic: keywords
subject: Fuel sloshing
  topic: keywords
subject: Aircraft pitch dynamics
  topic: keywords
subject: Chaotic motion
  topic: keywords
subject: Dynamical analysis
  topic: keywords
subject: Nonlinear Dynamics
  topic: subjects
subject: Mechanical Engineering
  topic: disciplines
subject: Nonlinear Dynamics
  topic: disciplines
subject: Fluid Mechanics
  topic: disciplines
subject: Control Systems Engineering
  topic: disciplines
subject: Applied Mathematics
  topic: disciplines
creator: Ons Kooli
creator: İhsan Pehlivan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 18
ending page: 25
doi: 10.69882/adba.cs.2026014

Stochastic Weather Simulation of Türkiye’s Geographical Regions: A Monte Carlo Framework
type: item
description: Understanding and predicting regional climatic variations is crucial for agricultural planning, water resource management, and disaster mitigation in the face of increasing global climate instability. This study presents a comprehensive stochastic simulation framework designed to model the diverse meteorological profiles of Türkiye’s seven geographical regions. The developed model utilizes a randomized structure that learns monthly parameters from historical daily datasets spanning the 2010–2024 period and generates daily temperature and precipitation scenarios for 2025. Methodologically, a Normal Distribution was employed for temperature modeling, while a first-order Markov Chain was utilized to determine the occurrence of precipitation. To account for the characteristically positive and right-skewed nature of rainfall intensity on wet days, the Gamma Distribution was preferred a standard approach in current literature for modeling daily precipitation amounts. To capture the inherent stochasticity of atmospheric processes, a Monte Carlo simulation approach was implemented. Each representative city was initially simulated with 300 iterations to ensure statistical robustness. To rigorously assess predictive accuracy, validation for 2024 was performed using a Monte Carlo simulation with 400 independent runs. The findings demonstrate that the model effectively captures regional climatic trends and provides a reliable synthetic dataset for environmental planning. Validation metrics indicate strong agreement between modeled and observed climatic behavior, with historical monthly temperatures, precipitation totals, and wet-day probabilities consistently falling within the simulated 95% confidence intervals. These results suggest that integrated stochastic models offer a high-fidelity and computationally efficient alternative to complex numerical weather predictors for regional climate assessment.
subject: Articles
  topic: section
subject: Monte carlo simulation
  topic: keywords
subject: Stochastic weather modeling
  topic: keywords
subject: Precipitation simulation
  topic: keywords
subject: Temperature modeling
  topic: keywords
subject: Markov chain
  topic: keywords
subject: Modeling
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: Nihan Hüsna Kılıç
creator: Abdullah Sevin
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 37
ending page: 50
doi: 10.69882/adba.cs.2026016

A Systematic Benchmark of Advanced Architectures for Automated Multi-Class Classification in Digital Pathology
type: item
description: Histopathological assessment of tissue biopsies is the main way to diagnose breast cancer. The current truth is that interpreting histopathological images is subjective and typically requires a great deal of effort by busy pathologists. Deep learning has transformed the practice of Digital Pathology, but at this moment, there is no universal agreement on which architecture gives the best performance for multiclass tissue recognition. The goal of this work is to analyze and compare the traditional Convolutional Neural Network (CNN), ResNet-101 and DenseNet-169, to the recently developed Transformer architecture, the Vision Transformer (ViT), by using a systematic benchmarking approach. Our approach involved using a balanced dataset with images from four classes (Benign, InSitu, Invasive, and Normal) and included preparation of images to a standardized input size of 224x224, transfer learning, and standard augmentations. Experimental results indicated that DenseNet-169 performs significantly greater than ResNet-101 (75% accuracy) with an improved accuracy of 96.25% and F1-score of 0.9628 at comparatively low levels of computational power (67.169 GFLOPs). DeiT Base is also an effective diagnostic adjunct, but due to its extensive number of parameters (85.80M) and computational cost, there are clear advantages in using optimized dense CNN architectures in limited clinical resources.
subject: Articles
  topic: section
subject: Convolutional neural networks
  topic: keywords
subject: Vision transformers
  topic: keywords
subject: Multi-class classification
  topic: keywords
subject: Comparative study
  topic: keywords
subject: Computer-aided diagnosis
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Histopathology
  topic: keywords
subject: Breast cancer
  topic: keywords
subject: Deep Learning
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: Mehmet Ali Eryuksel
creator: Yiğitcan Çakmak
creator: Ishak Pacal
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 57
ending page: 62
doi: 10.69882/adba.cs.2026018

Determination of Optimal Linear Congruence Generators Parameters with Heuristic Optimization Algorithms
type: item
description: Random number generators play a critical role in many scientific and engineering applications, particularly in simulation, cryptography, and optimization. Among these generators, Linear Congruential Generators (LCGs) are widely used due to their simplicity and computational efficiency. However, the statistical quality of the generated sequences strongly depends on the proper selection of generator parameters. In this study, the determination of optimal LCG parameters is formulated as a heuristic optimization problem. The Improved Grey Wolf Optimizer (IGWO) is employed to search for suitable multiplier, increment, and seed values. The proposed approach aims to achieve a uniform distribution of generated numbers while maintaining low correlation between consecutive values. The performance of the optimized LCG is evaluated using the objective fitness function as well as additional statistical performance metrics derived from the generated sequences. The effectiveness of the proposed IGWO-based optimization approach is demonstrated through repeated independent runs using the same fitness evaluation framework. Experimental results demonstrate that the proposed approach provides improved parameter selection for LCGs and enhances the statistical properties of the generated random sequences.
subject: Articles
  topic: section
subject: Linear congruential generator
  topic: keywords
subject: Random number generation
  topic: keywords
subject: Heuristic optimization
  topic: keywords
subject: Grey wolf optimizer
  topic: keywords
subject: Statistical randomness
  topic: keywords
subject: Optimization
  topic: subjects
subject: Computer Science, Mathematical Modeling, Dynamical Systems.
  topic: disciplines
creator: Sinem Akyol
creator: Fatih Özkaynak
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 1
ending page: 5
doi: 10.69882/adba.cs.2026011

Design of an ESP32-Based Smart Meteorological Data Collection Station for Renewable Energy Applications
type: item
description: This study presents the design and implementation of an ESP32 microcontroller based, modular smart meteorological data collection station developed to increase the efficiency of renewable energy systems and monitor environmental data at a low cost.The system is designed as an alternative to high-cost industrial stations. Core hardware components include BMP280 (temperature, pressure), wind speed (Hall Effect anemometer), and wind direction (Hall Effect wind vane) sensors. ESP32 is used as the central control unit with its built-in Wi-Fi/Bluetooth features; it processes data in string format and transmits it to a Python-based desktop interface via Bluetooth serial communication protocol (at a frequency of 1 Hz). Additionally, a one-week circular storage logic (Circular Buffer) was created using the LittleFS file system on the ESP32 for uninterrupted data storage. A solar panel and battery management system were designed for off-grid operation capacity. As a result of validation tests, it was proven that the system provides high stability in data transmission and that linear regression-based calibration (over 50% error improvement) is mandatory to reach professional standards, especially in atmospheric pressure measurements. This low-cost and energy-efficient platform aims to provide a scalable, domestic data collection infrastructure for renewable energy sites and smart agriculture projects.
subject: Articles
  topic: section
subject: Weather station
  topic: keywords
subject: Internet of things
  topic: keywords
subject: LittleFS
  topic: keywords
subject: ESP32
  topic: keywords
subject: Anemometer
  topic: keywords
subject: Wind vane
  topic: keywords
subject: Renewable energy
  topic: keywords
subject: Smart sensors
  topic: keywords
subject: Embedded Systems
  topic: subjects
subject: Renewable Energy Applications
  topic: disciplines
creator: Bedir Kaan Karapapak
creator: Mevlüt Eren Küçüker
creator: Mert Ali Topal
creator: Eren Emek
creator: Mohammad Soukar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 51
ending page: 56
doi: 10.69882/adba.cs.2026017

Hyperparameter Optimization for Big Data: Adapting Sampling Methods to Apache Spark MLlib
type: item
description: MLlib is an Apache Spark library that provides many machine learning algorithms and data processing utilities. Although the default configuration of these algorithms yields satisfactory results for practitioners, further tuning is often needed to improve resource usage efficiency. Furthermore, tuned MLlib algorithms may run faster than those using default configurations. However, this improvement depends on several factors, including machine settings, dataset design, and operating system preferences. Previous studies have generally focused on developing sophisticated tuners for MLlib, evaluating algorithm-focused optimizers for their competitiveness. Although derivative-based and model-free optimizers have been modified for use with MLlib, sampling-based optimizers are generally overlooked. To fill this research gap, this study empirically compares sampling-based and model-free techniques for tuning MLlib. Firstly, Monte Carlo and Cross-Entropy sampling algorithms are adapted to optimize MLlib algorithms. Subsequently, model-free techniques, including grid and random search algorithms, are compared with these sampling-based algorithms. Through extensive experimentation, their advantages and limitations are highlighted. Finally, threats to validity and future directions for unlocking the tuning potential of Apache Spark are discussed by interpreting performance bottlenecks and promising areas for optimization.
subject: Articles
  topic: section
subject: Optimization
  topic: keywords
subject: Apache spark
  topic: keywords
subject: Tuning
  topic: keywords
subject: Monte carlo
  topic: keywords
subject: Cross-entropy
  topic: keywords
subject: Optimization
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: M. Maruf Ozturk
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 6
ending page: 12
doi: 10.69882/adba.cs.2026012

Dynamics, Stability, and Bifurcation in Discrete-Time Predator-Prey Model
type: item
description: A discrete-time three-species food chain model is presented in this paper, focusing on bifurcation dynamics and chaos control. The transcritical and Neimark-Sacker bifurcations at distinct equilibrium points under specific parameter conditions are revealed by bifurcation and stability theory. Stabilizing chaotic dynamics is achieved using the Ott-Grebogi-Yorke (OGY) method. The dynamical behavior of the model is investigated by comparing phase portraits and time series across varying initial conditions. An assessment of stability is made, a topological classification is performed, and attractors are identified. Lyapunov exponent analysis also provides a deeper understanding of the system’s complex behavior. In food chain models based on population, numerical simulations verify theoretical results by revealing the complex interplay among bifurcations, chaos, and control mechanisms.
subject: Articles
  topic: section
subject: Modeling FCM
  topic: keywords
subject: Bifurcation
  topic: keywords
subject: Topological classification
  topic: keywords
subject: Chaos control
  topic: keywords
subject: Maximum lyapunov exponent
  topic: keywords
subject: Time series
  topic: keywords
subject: Nonlinear Dynamics
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: Ansar Abbas
creator: Abdul Khaliq
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 26
ending page: 36
doi: 10.69882/adba.cs.2026015

Microcontroller Execution, Random Number Generation and Chaos Annihilation in Piecewise Quadratic Map
type: item
description: This paper presents the study of microcontroller execution, pseudo random number generator (PRNG) and chaos annihilation in a piecewise quadratic map (PQM). PQM is generated by replacing the cubic nonlinearity of cubic map by absolute nonlinearity. Bistable outphase and monostable chaotic characteristics and bistable outphase periodic oscillations are encountered in PQM during numerical simulations. The microcontroller execution of PQM is realized to validate the numerical results encountered in PQM. The PRNG derived from the PQM is made and the NIST 800-22 statistical test validated it. Finally, analytical calculations and numerical simulations show the effectiveness of chaos annihilation in PQM using feedback controller.
subject: Articles
  topic: section
subject: Chaotic maps
  topic: keywords
subject: Microcontroller execution
  topic: keywords
subject: RNG
  topic: keywords
subject: Chaos annihilation
  topic: keywords
subject: Nonlinear Dynamics
  topic: subjects
subject: Physics
  topic: disciplines
creator: Lucienne Makouo
creator: Jules Metsebo
creator: Cyrille Ainamon
creator: Oumate Alhadji Abba
creator: André Chéagé Chamgoué
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
date: 2026-01-31
publication date: 2026-01-31
starting page: 13
ending page: 17
doi: 10.69882/adba.cs.2026013

A Mobile App for Enhancing Suture Skills through XAI
type: item
description: This study aims to enhance the suturing skills of medical practitioners by leveraging artificial intelligence (AI) techniques. Initially, a dataset of sutures was obtained from a hospital setting and underwent preprocessing to align with the model requirements. Subsequently, data augmentation was applied to enhance the dataset for improved performance. Using transfer learning, a classification algorithm was trained on the augmented dataset with %96.59 training and %79.24 validation accuracy. To ensure interpretability, SHAP (SHapley Additive exPlanations) analysis was employed to explain the decisions made by the classification algorithm, revealing the influential pixels in suture success. In the final stage, users were introduced to the project via a mobile application developed with Flutter and Dart. This app allows users to capture images of their sutures, which are then uploaded for analysis. The SHAP analysis results are presented visually to users, indicating which parts of the suture are deemed successful and which are not via heatmaps. By providing this feedback loop, the application aims to assist medical professionals in assessing and improving their suturing skills. This study presents a valuable tool for evaluating and enhancing medical suturing abilities, with potential implications for medical education and practice. In the future this preliminary study will be test with application users which will provide continuous feedback for model refinement.
subject: Articles
  topic: section
subject: Suture
  topic: keywords
subject: Transfer learning
  topic: keywords
subject: SHAP analysis
  topic: keywords
subject: Mobile application
  topic: keywords
subject: Medical education
  topic: keywords
creator: Eda Nur Cumak
creator: Turker Berk Donmez
creator: Onur Kutlu
creator: Mustafa Kutlu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 43
ending page: 49
doi: 10.69882/adba.cs.2025073

True Random Number Generator Design with A Fractional Order Sprott B Chaotic System
type: item
description: The growing prevalence of digital communication and interconnected devices has amplified the need for robust data security measures. True random number generators (TRNG) play a pivotal role in protecting information by generating unpredictable and irreproducible sequences required for encryption, secure authentication, and cryptographic key generation. This research presents a TRNG model based on the fractional-order Sprott B chaotic system. The chaotic properties of the system were confirmed through Lyapunov exponent calculations, bifurcation diagrams, and phase space analyses. The fractional-order dynamics enhance the complexity and unpredictability of the generated entropy source, making it suitable for secure applications. The performance of the generated random numbers was assessed using the NIST 800-22 statistical test suite, successfully passing all tests and meeting the randomness requirements. This study introduces a unique approach by leveraging the fractional-order Sprott B chaotic system for TRNG design, demonstrating its effectiveness in cryptographic systems and secure communication frameworks.
subject: Articles
  topic: section
subject: Nonlinear dynamics
  topic: keywords
subject: Chaos
  topic: keywords
subject: TRNG
  topic: keywords
subject: Fractional-order chaotic systems
  topic: keywords
creator: Mehmet Ziya Hosbas
creator: Berkay Emin
creator: Fırat Kacar
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 50
ending page: 55
doi: 10.69882/adba.cs.2025074

Chaos Annihilation-Based on Genetic Algorithms in a Non-smooth Air-gap Permanent Magnet Synchronous Motor Embedded in the Microcontroller
type: item
description: This paper is devoted to the dynamical probing, microcontroller execution, and chaos annihilation based on genetic algorithms (GAs) in a non-smooth air-gap permanent magnet synchronous motor (NSAGPMSM) without external disturbances. The numerical simulations of NSAGPMSM in the absence of external disturbances reveal the existence of two different shapes of chaotic characteristics, reverse period doubling and periodic characteristics. The dynamical characteristics are certified by the microcontroller execution of NSAGPMSM in the absence of external disturbances. By optimizing the parameters of NSAGPMSM in the absence of external disturbances, thanks to the convergence of the objective function and its convergence rate, it is proven that GAs is effective in optimizing system parameters, leading to annihilation of chaos in the system to one of its three steady states.
subject: Articles
  topic: section
subject: Non-smooth-air-gap
  topic: keywords
subject: Permanent magnet synchronous motor
  topic: keywords
subject: Microcontroller execution
  topic: keywords
subject: Annihilation of chaos
  topic: keywords
subject: Genetic algorithms
  topic: keywords
creator: Isidore Komofor Ngongiah
creator: Rolande Tsapla Fotsa
creator: Joseph Mvogo Ngono
creator: Noel Nyang Kibanya
creator: Emile Godwe
creator: Foutse Momo
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 30
ending page: 35
doi: 10.69882/adba.cs.2025071

Computational Study of the Effect of Aluminum Content on the Thermodynamic Properties and Phase Stabilityof Mg-Al-Zn Alloys
type: item
description: This study investigates the effect of varying aluminum (Al) content on the thermodynamic properties and phase stability of Mg-Al-Zn alloys using Computational Materials Science techniques. Three Mg-Al-Zn alloys were modeled with 3 wt%, 6 wt%, and 9 wt% Al, while the zinc (Zn) content was fixed at 1 wt% and magnesium (Mg) made up the remaining balance. Phase fraction diagrams were generated using the JMatPro software to analyze the phase transformations and their evolution with temperature. Additionally, the phase compositions and thermodynamic behavior of the alloys at a fixed temperature of 300 °C were examined. The chemical potential (Mμ) of each element was calculated to assess the energy state and stability of Mg, Al, and Zn in the alloys. Furthermore, the activity values of the elements were determined to evaluate their deviations from ideal behavior and effective thermodynamic concentrations within the alloys. The results reveal that increasing Al content significantly influences the phase stability and transformation behavior of the Mg-Al-Zn system. Higher Al concentrations led to a greater proportion of the intermetallic phase (Mg17​Al12) at 300 °C, which in turn affected the chemical potential and activity of the constituent elements. This computational investigation provides insights into the role of Al in tailoring the thermodynamic properties and phase constitution of Mg-Al-Zn alloys, offering valuable guidance for alloy design and optimization in lightweight structural applications.
subject: Articles
  topic: section
subject: Computational materials science
  topic: keywords
subject: Magnesium alloys
  topic: keywords
subject: Thermodynamic properties
  topic: keywords
creator: Yusuf Uzunoğlu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 36
ending page: 42
doi: 10.69882/adba.cs.2025072

Demystifying English Towns Educational Outcomes with Explainable Artificial Intelligence
type: item
description: Explainable Artificial Intelligence has emerged as a critical tool in addressing the transparency challenges associated with machine learning models. This study investigates the application of XAI techniques in the educational domain, with a focus on identifying factors influencing academic performance. Using datasets encompassing student demographics, academic achievements, and contextual variables, machine learning models were developed and analyzed using SHapley Additive exPlanations. The results highlighted the significance of higher qualification achievements and early academic milestones, such as num_level_3_at_age_18 and num_key_stage_2_attainment. These findings corroborate existing literature while providing novel insights through visual and interpretable analytics. The study demonstrates the transformative potential of XAI in uncovering actionable insights, offering policymakers and educators tools to address disparities in educational outcomes. The novelty of applying XAI in this context lies in its ability to bridge the gap between complex predictive models and practical decision-making. Future research directions include expanding datasets to incorporate diverse educational settings and developing real-time educational tools based on interpretability insights. This work lays the foundation for leveraging XAI to drive equity and excellence in education.
subject: Articles
  topic: section
subject: Rural education
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Town education
  topic: keywords
subject: Educational outcomes
  topic: keywords
creator: Burcu Kutlu
creator: Mustafa Kutlu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 15
ending page: 18
doi: 10.69882/adba.cs.2025013

Bibliometric Analysis of Studies on Cyber Crimes Between 2000-2023
type: item
description: The increasing use of internet-based technologies and computer networks, which grow and develop daily, has brought problems. A new type of crime called cybercrime has emerged and is committed through computers. There are various research and studies on cyber crimes. This study presents a bibliometric analysis of studies on the keywords "Cyber Crimes", "Internet Crimes" and "Computer Crimes" indexed in Web of Science between 2000 and 2023. This study aims to reveal the scientific map of research and studies on cybercrimes, make sense of the data, understand the current situation, trends, and relationships in this field, and create a resource for future cybercrime studies. Bibliometric analyzes were performed using Bibliometrix and Microsoft Excel programs. A total of 2566 studies by 5590 different authors were determined to be used in the research. Jaishankar, K. was the most prolific writer with 21 articles. University College Dublin was the university with the most publications, with 56 articles. IEEE Access became the most-published journal with 151 articles. The most cited work is Stuxnet and the Future of Cyber Wars by Farwell, JP, and Rohozinski, R. Bibliometric analysis results such as most used keywords, including the most influential and productive countries, authors, universities and journals in the field of cybercrime are included. In studies on cybercrime, the relationships and collaborations between countries and authors are presented with visuals.
subject: Articles
  topic: section
subject: Cyber
  topic: keywords
subject: Cyber crimes
  topic: keywords
subject: Bibliometric analysis
  topic: keywords
subject: Citation analysis
  topic: keywords
subject: Internet crimes
  topic: keywords
creator: Murat Erdoğan
creator: Ömer Faruk Akmeşe
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
date: 2025-01-31
publication date: 2025-01-31
starting page: 19
ending page: 29
doi: 10.69882/adba.cs.2025014

High-Accuracy Prediction of Mechanical Properties of Ni-Cr-Fe Alloys Using Machine Learning
type: item
description: Artificial intelligence-driven prediction models have emerged as powerful tools for estimating material properties with high accuracy, yet the preparation of training datasets often demands labor-intensive and time-consuming experimental procedures. Leveraging the Computational Materials Science (CMS) approach, this study utilizes phase transformation calculations and thermodynamic data to simulate the mechanical properties of Ni-Cr-Fe alloys. Using JMatPro software, mechanical properties (0.2% Proof Stress, Fracture Stress, and Young's Modulus) of 50 Ni-Cr-Fe alloy compositions were simulated across a temperature range of 540–920°C, generating a dataset of 1000 rows. This dataset was used to train an Artificial Neural Network (ANN) model, with 80% allocated for training and 20% for validation and testing. The trained AI model demonstrated robust predictive capabilities, achieving a 96,61% accuracy rate in forecasting material compositions with the desired thermo-physical properties at specific temperatures. To validate the model's reliability, predicted alloy compositions were re-simulated under identical conditions in JMatPro, confirming the high fidelity of the model's predictions. The results underscore the efficacy of Computational Materials Science (CMS)-generated datasets as a scalable and reliable source for training AI models in materials science. This study highlights the potential of integrating Computational Materials Science (CMS) and Machine Learning approaches to accelerate material design and development processes, delivering significant improvements in prediction speed and accuracy.
subject: Articles
  topic: section
subject: Computational materials science
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Artificial neural networks
  topic: keywords
subject: Alloy design
  topic: keywords
subject: Nickel alloys
  topic: keywords
creator: Yusuf Uzunoğlu
creator: Berkay Emin
creator: Yusuf Alaca
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
date: 2025-01-31
publication date: 2025-01-31
starting page: 7
ending page: 14
doi: 10.69882/adba.cs.2025012

The Role of Technological Approaches in Cyber Security of Autonomous Vehicles
type: item
description: Autonomous vehicles play a significant role in future transportation systems by enabling driverless travel. These vehicles offer advantages such as reducing accidents, improving travel times, and conserving energy. However, ensuring the independent operation of these technologies requires robust cybersecurity measures. Protecting autonomous vehicles against security threats they may face within their internal systems or during communication with other vehicles and infrastructure is crucial. This study examines the security measures used in autonomous vehicles. Encryption and data protection techniques safeguard information from unauthorized access during in-vehicle and inter-vehicle communication. Additionally, intrusion detection and prevention systems (IDS/IPS) detect abnormal activities to protect against potential threats. Machine learning-based anomaly detection methods analyze data from sensors and network traffic to identify emerging threats. Regular software updates help mitigate vulnerabilities, while network segmentation isolates different systems to protect critical components. Multi-layered security solutions ensure the safe operation of autonomous vehicles. These approaches contribute to the development of future security standards.
subject: Articles
  topic: section
subject: Autonomous vehicles
  topic: keywords
subject: Cybersecurity threats
  topic: keywords
subject: Autonomous vehicle security
  topic: keywords
subject: Artificial intelligence
  topic: keywords
creator: Ebubekir Seyyarer
creator: Faruk Ayata
creator: Selim Özdem
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
date: 2025-01-31
publication date: 2025-01-31
starting page: 1
ending page: 6
doi: 10.69882/adba.cs.2025011

Evaluating the Effectiveness of Machine Learning Models in Predicting Student Academic Achievement
type: item
description: Student performance is influenced by various factors, including demographic, psychological, social, and institutional determinants. This study comprehensively analyzes these factors to understand their relative impacts on academic achievement. Data from 1000 students, including variables such as gender, ethnicity, parental education level, lunch type, test preparation course participation, and scores in mathematics, reading, and writing, were analyzed. Categorical variables were converted to numerical values and normalized, followed by exploratory data analysis and correlation analysis. Machine learning models, including Linear Regression, Decision Tree Regressor, Random Forest Regressor, K-Nearest Neighbors Regressor, Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), XGBoost Regressor, and Neural Network (MLP), were used to predict student performance. The study found that test preparation courses significantly enhance student performance, with strong positive correlations between mathematics, reading, and writing scores. Demographic factors such as gender, ethnicity, parental education level, and lunch type were not direct determinants of student performance. The SVR and Linear Regression models exhibited the best predictive performance. These findings highlight the need for optimized educational strategies focusing on effective test preparation and the interconnectedness of subject performance to enhance academic achievement.
subject: Articles
  topic: section
subject: Academic achievement
  topic: keywords
subject: Linear regression
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Student performance
  topic: keywords
subject: Academic Achievement
  topic: subjects
subject: Linear Regression
  topic: subjects
subject: Machine Learning
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: Emre Deniz
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-01
publication date: 2024-07-01
starting page: 8
ending page: 13
doi: 10.69882/adba.cs.2024072

Deep Learning in Agriculture: Detection and Analysis of Sugar Beets with YOLOv8
type: item
description: In this study, the performance of the YOLOv8 model in detecting sugar beets was evaluated using images obtained from a drone over a sugar beet field. High-resolution drone images were divided into small segments, labeled, and the model was trained using data augmentation techniques. The results obtained during the training and testing phases demonstrated that the model successfully detected sugar beets with high accuracy, precision, recall, and F1 score values. The analysis of label correlograms and result graphs confirmed the model's labeling accuracy and detection capability. These findings indicate that the YOLOv8 model can be an effective tool in agricultural production monitoring and plant health assessment applications. In the future, the model's performance will be more comprehensively evaluated using datasets obtained from different geographical regions and various agricultural products.
subject: Articles
  topic: section
subject: Sugar beet detection
  topic: keywords
subject: Drone images
  topic: keywords
subject: Deep learning
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Agricultural monitoring
  topic: keywords
subject: Sugar beet detection
  topic: subjects
subject: drone images
  topic: subjects
subject: deep learning
  topic: subjects
subject: YOLOv8
  topic: subjects
subject: agricultural monitoring
  topic: subjects
creator: Cem Özkurt
creator: Firdevs Sungu
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-01
publication date: 2024-07-01
starting page: 1
ending page: 7
doi: 10.69882/adba.cs.2024071

Optimizing Diabetes Prediction: Addressing Data Imbalance with Machine Learning Algorithms
type: item
description: Imbalanced datasets pose significant challenges in various fields, including the classification of medical conditions such as diabetes. This study investigates six methodologies for handling imbalanced diabetes datasets, aiming to enhance classification performance through diverse preprocessing techniques. The methodologies are evaluated using multiple models: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, SVM, KNN, Naive Bayes, XGBoost, LightGBM, and CatBoost. The preprocessing techniques include simple implementation, data standardization, normalization, standardization with K Fold cross-validation, and two variations incorporating the SMOTE oversampling technique.The effectiveness of each methodology is assessed based on accuracy, precision, recall, and F1 scores across different classifiers. Results indicate that standardization combined with K Fold cross-validation consistently enhances model performance. Additionally, the integration of the SMOTE technique significantly improves results, especially for Gradient Boosting and SVM classifiers. Among the tested models, CatBoost demonstrated exceptional performance in handling imbalanced datasets, achieving an accuracy of 95.18%, precision of 91.10%, recall of 95.52%, and an F1 score of 93.26%. This study underscores the importance of tailored preprocessing techniques in improving the classification of imbalanced medical datasets, highlighting their potential to enhance predictive accuracy in critical applications.
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: Imbalanced dataset
  topic: keywords
subject: Diabetes classification
  topic: keywords
subject: Ensemble learning
  topic: keywords
subject: Machine learning
  topic: disciplines
creator: Khalid Hani Abushahla
creator: Muhammed Ali Pala
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-01
publication date: 2024-07-01
starting page: 26
ending page: 35
doi: 10.69882/adba.cs.2024075

Enhancing Anomaly Detection in Large-Scale Log Data Using Machine Learning: A Comparative Study of SVM and KNN Algorithms with HDFS Dataset
type: item
description: As information technology rapidly advances, servers, mobile, and desktop applications are easily attacked due to their high value. Therefore, cyber attacks have raised great concerns in many areas. Anomaly detection plays a significant role in the field of cyber attacks, and log records, which record detailed system runtime information, have consequently become an important data analysis object. Traditional log anomaly detection relies on programmers manually inspecting logs through keyword searches and regular expression matching. While programmers can use intrusion detection systems to reduce their workload, log data is massive, attack types are diverse, and the advancement of hacking skills makes traditional detection inefficient. To improve traditional detection technology, many anomaly detection mechanisms, especially machine learning methods, have been proposed in recent years. In this study, an anomaly detection system using two different machine learning algorithms is proposed for large log data. Using Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) algorithms, experiments were conducted with the Hadoop Distributed File System (HDFS) log dataset, and experimental results show that this system provides higher detection accuracy and can detect unknown anomaly data.
subject: Articles
  topic: section
subject: Anomaly Detection
  topic: keywords
subject: KNN
  topic: keywords
subject: SVM
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: HDFS
  topic: keywords
subject: Computer Security
  topic: subjects
subject: Machine Learning
  topic: subjects
creator: Yusuf Alaca
creator: Erdal Basaran
creator: Yüksel Celik
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-01
publication date: 2024-07-01
starting page: 14
ending page: 18
doi: 10.69882/adba.cs.2024073

Environmental Sustainability through AI: A Case Study on CO2 Emission Prediction
type: item
description: In this study, the Biogeography-Based Optimization (BBO) algorithm has been effectively utilized to predict carbon dioxide (CO2) emissions. BBO is a nature-inspired optimization method used to analyze complex relationships and determine significant features in a dataset. The focus of the study is to accurately predict the "share_global_coal_co2" parameter, and for this purpose, the BBO algorithm has been used to identify the most effective 20 features. The analyses revealed that the Gradient Boosting algorithm provided the lowest Mean Squared Error (MSE) value (0.347408), indicating that the model could make predictions closer to the real data. Additionally, with the use of interpretable artificial intelligence models such as SHAP and LIME, it was determined that the model's predictions became more understandable, and the effects of specific features on the predictions were clarified. The obtained results provide important guidance for environmental policymakers and energy experts. The effectiveness of the BBO algorithm in predicting CO2 emissions can contribute to making more knowledge-based and data-driven decisions in environmental analysis and policymaking processes. This study highlights the importance of artificial intelligence and optimization techniques in achieving sustainability goals and helps in developing more effective strategies in environmental management.
subject: Articles
  topic: section
subject: CO2 emissions
  topic: keywords
subject: Biogeography- Based optimization (BBO)
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: SHAP
  topic: keywords
subject: LIME
  topic: keywords
subject: CO2 Emissions
  topic: subjects
subject: Biogeography-Based Optimization (BBO)
  topic: subjects
subject: Explainable AI
  topic: subjects
subject: SHAP
  topic: subjects
subject: LIME
  topic: subjects
creator: Cem Özkurt
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-01
publication date: 2024-07-01
starting page: 19
ending page: 25
doi: 10.69882/adba.cs.2024074