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Predictive Modeling for Milk Quality Using Machine Learning and XAI Algorithms
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 applied to analyze the contributions of key features influencing milk quality. Experimental results indicate that physicochemical properties, particularly pH, fat content, and temperature, play a decisive role in milk quality prediction. In conclusion, this study demonstrates that the combined use of machine learning and explainability techniques provides significant contributions in terms of reliability, transparency, and methodological robustness in milk quality classification.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
Chaos Analysis of a Fuel-Slosh Coupled Aircraft System
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.
Stochastic Weather Simulation of Türkiye’s Geographical Regions: A Monte Carlo Framework
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.
A Systematic Benchmark of Advanced Architectures for Automated Multi-Class Classification in Digital Pathology
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.
Determination of Optimal Linear Congruence Generators Parameters with Heuristic Optimization Algorithms
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.
Computer Science, Mathematical Modeling, Dynamical Systems.
Design of an ESP32-Based Smart Meteorological Data Collection Station for Renewable Energy Applications
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.
Hyperparameter Optimization for Big Data: Adapting Sampling Methods to Apache Spark MLlib
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.
Dynamics, Stability, and Bifurcation in Discrete-Time Predator-Prey Model
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.
Microcontroller Execution, Random Number Generation and Chaos Annihilation in Piecewise Quadratic Map
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.
A Mobile App for Enhancing Suture Skills through XAI
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
True Random Number Generator Design with A Fractional Order Sprott B Chaotic System
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.
Chaos Annihilation-Based on Genetic Algorithms in a Non-smooth Air-gap Permanent Magnet Synchronous Motor Embedded in the Microcontroller
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.
Computational Study of the Effect of Aluminum Content on the Thermodynamic Properties and Phase Stabilityof Mg-Al-Zn Alloys
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.
Demystifying English Towns Educational Outcomes with Explainable Artificial Intelligence
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.
Bibliometric Analysis of Studies on Cyber Crimes Between 2000-2023
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
High-Accuracy Prediction of Mechanical Properties of Ni-Cr-Fe Alloys Using Machine Learning
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.
The Role of Technological Approaches in Cyber Security of Autonomous Vehicles
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.
Evaluating the Effectiveness of Machine Learning Models in Predicting Student Academic Achievement
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.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Deep Learning in Agriculture: Detection and Analysis of Sugar Beets with YOLOv8
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.
Optimizing Diabetes Prediction: Addressing Data Imbalance with Machine Learning Algorithms
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.
Enhancing Anomaly Detection in Large-Scale Log Data Using Machine Learning: A Comparative Study of SVM and KNN Algorithms with HDFS Dataset
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.
Environmental Sustainability through AI: A Case Study on CO2 Emission Prediction
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.