Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

This paper describes one of the non-linear (and non-stationary) stochastic models, the GSB (Gaussian, or Generalized, Split-BREAK) process, which is used in the analysis of time series with pronounced and accentuated fluctuations. In the beginning, the stochastic structure of the GSB process and its important distributional and asymptotic properties are given. To that end, a method based on characteristic functions (CFs) was used. Various procedures for the estimation of model parameters, asymptotic properties, and numerical simulations of the obtained estimators are also investigated. Finally, as an illustration of the practical application of the GSB process, an analysis is presented of the dynamics and stochastic distribution of the infected and immunized population in relation to the disease COVID-19 in the territory of the Republic of Serbia.

Computer adaptive testing (CAT) enables an individualization of tests and better accuracy of knowledge level determination. In CAT, all test participants receive a uniquely tailored set of questions. The number and the difficulty of the next question depend on whether the respondent’s previous answer was correct or incorrect. In order for CAT to work properly, it needs questions with suitably defined levels of difficulty. In this work, the authors compare the results of questions’ difficulty determination given by experts (teachers) and students. Bachelor students of informatics in their first, second, and third year of studies at Subotica Tech—College of Applied Sciences had to answer 44 programming questions in a test and estimate the difficulty for each of those questions. Analyzing the correct answers shows that the basic programming knowledge, taught in the first year of study, evolves very slowly among senior students. The comparison of estimations on questions difficulty highlights that the senior students have a better understanding of basic programming tasks; thus, their estimation of difficulty approximates to that given by the experts.

This paper explores the optimization of firewall parameters for attack detection using machine learning techniques, focusing on improving network security in dynamic environments. Traditional firewall systems often face limitations in detecting malicious traffic due to static rule sets and high false positive rates, particularly in real-world scenarios with evolving attack patterns. To overcome these challenges, this study applies neural networks with the rectified linear unit activation function (ReLU), which enables precise attack detection and real-time firewall policy adjustments. The proposed 5-5-4 neural network model, tested using real-world datasets, achieved an accuracy of 96.3%, outperforming alternative configurations. The analysis evaluated three scenarios: normal conditions, active attacks, and post-policy adjustment, confirming the effectiveness in enhancing detection and mitigation capabilities. The results highlight the potential of machine learning, particularly neural networks, as a robust tool to improve network security. This approach enables future integration of IoT and real-time threat monitoring.

The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%.

Digital image forgery detection is a vital area of research in digital forensics, aiming to authenticate visual content in the face of increasingly sophisticated manipulation techniques. This paper presents a comprehensive overview of the field, integrating key concepts in its technical landscape. The categorization of detection methods typically includes active approaches that depend on embedded watermarks or signatures, and passive (blind) techniques that analyze the image content itself without prior information. Underlying these are various detection principles, such as identifying inconsistencies in pixel patterns, compression artifacts, illumination, and sensor noise. The paper explores the specific characteristics of detection techniques, analyzing their strengths and limitations. Pixel-based and statistical methods offer efficiency for copy? move and splicing detection but often lack robustness under compression or scaling. Frequency-domain methods and physics-based analysis provide deeper insights, but they can be computationally intensive or sensitive to environmental conditions. The evaluation of detection models is crucial, relying on diverse datasets, realistic manipulation scenarios, and adversarial robustness testing. Effective evaluation metrics include accuracy, precision, recall, F1-score, AUC-ROC and IoU, which collectively assess classification and localization performance. The deep learning approach in forgery detection has significantly advanced the field, with convolutional neural networks and transformer-based models learning complex tampering artifacts. However, challenges persist in forgery detection, including evolving manipulation methods, dataset limitations, explainability concerns, and vulnerability to adversarial attacks. Finally, the authors discuss trends and future directions, such as self-supervised learning, multimodal forensic integration, domain adaptation, and real-time detection frameworks, paving the way for more resilient and scalable forensic tools.