The information stage of human society development which began at the end of the last century results in the fact that the state of information security has become directly dependent not only on the information processing technical systems and features but also on the perception of information at the level of individual psychological qualities. The use of information aggression and special information operations including those performed in modern geopolitics at the international and domestic levels for population management, during electoral campaigns is gaining enormous scope. The tasks of early information impact detection, situation development modeling in the information space necessitate the development of specialized models reproducing information confrontation. The major contradiction in the development of such models is that the more relevant and adaptive these models are the more complex and resource-intensive they become. At the same time, oversimplifying the information confrontation process makes such models inconsistent with real risks. This article gives a brief overview of modern information confrontation models and concepts. It is described the basic principles of the construction of an information confrontation ontological model: such key elements as subjects, objects, actual impacts, and the basic characteristics of each element are identified. An attempt has been made to develop a universal information confrontation ontological model. It has been also proposed a multipart tuple of information confrontation representation. This article is the beginning of a separate research project on information confrontation modeling, which will be further developed in papers to follow.

This study focuses on the opportunities and challenges of online learning in Bangladeshi higher education, particularly in achieving Sustainable Development Goal 4, emphasizing inclusive, equitable, and quality education for all. A well-structured questionnaire was administered to collect data from undergraduate students at a public university in Bangladesh. Students’ responses were analyzed quantitatively, using descriptive statistics and Chi-Square tests. Quantitative findings on student perceptions reveal significant benefits in terms of flexibility, self-paced learning, cost-effectiveness, and global networking opportunities, improving access and learning experiences among students from remote or marginal areas. On the other hand, the results draw attention to certain significant issues, such as dependence on technology, unsuitable learning environments, social isolation, and receiving feedback very late, which reduce online education success. These challenges underpin the pressing need for considerable investment in the digital infrastructure of affordable access to technology and blended models of learning, integrating online and offline resources that will meet the diverse needs of learners. Improvement in feedback mechanisms, facilitation of online communities of collaboration, and development of digital literacy among both students and educators contribute to better learning outcomes. The research has proposed actionable measures to enhance online education in Bangladesh, such as expanding internet connectivity, providing subsidies for devices, and implementing innovative teaching methods. Addressing these challenges while leveraging the benefits of online learning has the potential to transform higher education in Bangladesh. This will ensure greater accessibility, equity and inclusivity, thereby contributing significantly to the achievement of Sustainable Development Goal 4.

Information about the arrival of floods in rivers must be informed as soon as possible to the community so that it can save people along the river and its surroundings from the dangers of flooding which are very detrimental. Arduino Nano and Microcontroller ESP8266 provide good performance in providing information about the arrival of floods quickly. The working system of the tool is based on a water level sensor installed in the upstream area of the river which will be received and processed by Arduino nano, then the sensor data is communicated to the ESP8266 device (as a wifi node). Furthermore, ESP8266 will send information to the Android application. This system is very cost-effective and has low power consumption. Flood information will be sent to people along the river that flows through the city and residential areas. The test results show that the current system is functioning well, and is useful for flood monitoring systems in rivers.

Soil image classification plays a crucial role in agricultural and environmental practices. Traditional methods of soil classification often involve manual labor, which can be time-consuming and prone to human error. Recent advances in computer vision and machine learning have opened new horizons for automating this classification process. This research paper presents a comprehensive study and evaluates the performance of four convolutional neural network (CNN) architectures a custom CNN, ResNet50, InceptionV3, and MobileNetV2 on a custom soil image dataset comprising 1800 labelled images across four soil classes such as Black, Laterite, Red and White. The dataset created using smartphone camera to captured images under varying natural conditions. The objective of this work is to explore the effectiveness and accuracy of different machine learning algorithms used in categorizing soil types based on visual data. Each model’s performance is evaluated in terms of classification accuracy, precision, recall, and F1-score. Results indicate that ResNet50 achieves the highest accuracy 97.3%, followed closely by MobileNetV2 94.7%. The custom CNN, while computationally efficient, achieved 88.2%. We conclude that transfer learning with deep CNNs is highly effective for soil classification, and MobileNetV2 is a strong recommended for mobile applications. The comparative analysis demonstrates their effectiveness in distinguishing between different soil types, textures, and compositions. It also highlights how important it is to select the appropriate CNN architectures for certain tasks related to soil classification.  This work belongs to the increasing collection of information at the interface between soil science and computer vision. It offers a strategy to apply sophisticated deep learning-based algorithms to assess soil type more reliably and effectively, serving as a springboard for future research in the field of soil image analysis and classification.

Many study programs at universities face issues, including students experiencing delays in graduation, which hinders the completion of their studies on time. These delays in student graduation contribute to a decrease in the accreditation score of the Information Systems program. One solution to address this issue is to develop a data-mining-based system to monitor and utilize student progress data by predicting their graduation status using the C4.5 Decision Tree algorithm. This research process involves several stages: problem analysis, data and system design, coding, testing, and finally, maintenance. The outcome of this research is the implementation of the C4.5 algorithm to predict students' timely and delayed graduation. The data used includes records of students who graduated in 2021 and 2022. The acceptance rate, calculated using a confusion matrix, demonstrates an accuracy level of 92.16%, based on a dataset of 119 training data points and 51 testing data points, or 70% training to 30% testing ratio. The results of this research and testing indicate that the C4.5 Decision Tree algorithm is highly suitable for predicting student graduation outcomes.

Detecting kidney stones in coronal CT images remains challenging due to the small size of stones, anatomical complexity, and noise from surrounding objects. To address these challenges, we propose a deep learning architecture that augments a Vision Transformer (ViT) with a pre-processing module. This module integrates CSPDarknet for efficient feature extraction, a Feature Pyramid Network (FPN), and Path Aggregation Network (PANet) for multi-scale context aggregation, along with convolutional layers for spatial refinement. Together, these trained components filter irrelevant background regions and highlight kidney-specific features before classification by ViT, thereby improving accuracy and efficiency. This design leverages ViT’s global context modeling while mitigating its sensitivity to irrelevant regions and limited data. The proposed model was evaluated on two coronal CT datasets (one public and one private dataset) comprising 6,532 images under six experimental scenarios with varying training and testing conditions. It achieved 99.3% accuracy, 98.7% F1-score, and 99.4% mAP@0.5, higher than both YOLOv10 and the baseline ViT. The model contains 61.2 million parameters and has a computational cost of 37.3 GFLOPs, striking a balance between ViT (86.0M, 17.6 GFLOPs) and YOLOv10 (22.4M, 92.0GFLOPs). Despite having more parameters than YOLOv10, the model achieved a lower inference time than YOLOv10, approximately 0.06 seconds per image on an NVIDIA RTX 3060 GPU. These findings suggest the potential of our approach as a foundation for clinical decision-support tools, pending further validation on heterogeneous and challenging clinical datasets such as small (<2 mm) or low-contrast stones.

Heart attacks continue to be one of the primary causes of death globally, highlighting the critical need for advanced predictive models to improve early diagnosis and timely intervention. This study presents a comprehensive machine learning (ML) approach to heart attack prediction, integrating multiple datasets from diverse sources to construct a robust and accurate predictive model. The research employs a stacking ensemble model, which combines the strengths of individual ML algorithms to improve overall performance. Extensive data preprocessing steps were carefully undertaken to preserve the dataset's integrity and maintain its quality. The results demonstrate a superior accuracy of 97.48%, significantly outperforming state-of-the-art approaches. The high level of accuracy indicates the model’s potential effectiveness in the clinical setting for early detection of heart attack and prevention. However, the proposed model is influenced by the quality and diversity of the integrated datasets, which could affect its generalizability across broader populations. Challenges encountered during the model's development include optimizing hyperparameters for multiple classifiers, ensuring data preprocessing consistency, and balancing computational efficiency with model interpretability. The results underscore the pivotal contribution of advanced ML approaches in revolutionizing the management of cardiovascular attack. By addressing the complexities and variabilities inherent in heart attack prediction, the work provides a pathway towards more effective and personalized cardiovascular disease management strategies, demonstrating the transformative potential of ML in healthcare.

Pseudo Random Number Generators (PRNGs) are deterministic and periodic in nature. Hybrid Pseudo Random Number Generators (HPRNGs) address some limitations by using time-based seeding with a modified Linear Congruential Generator (LCG). While HPRNGs improve upon the deterministic nature by using dynamic time-based seeds, they still suffer from periodicity and potential seed-related issues. This study addresses the deterministic nature further as well as the periodicity of PRNGs by proposing an enhanced HPRNG, making it more suitable for high-security applications.

In the context of ongoing digitalization and the growing importance of non-formal education in Kazakhstan’s higher education system, there is an increasing demand for adaptive educational models that address students' individual learning needs and broaden the scope of academic engagement. This study examines the effects of an adaptive non-formal education model on students' learning activity and engagement, and identifies the model components with the most significant impact. A quantitative quasi-experimental design was employed, involving pre- and post-intervention assessments using validated questionnaires. Key indicators included participation in supplementary educational activities, online learning platforms, external courses, and project-based or volunteer initiatives. The results indicate a statistically significant improvement in students’ educational involvement in the experimental group, as demonstrated by increased participation in external learning events, greater self-directed learning, and the development of personalized educational trajectories. The study highlights the potential of adaptive non-formal education as a strategic tool to enhance institutional flexibility and student motivation. Its novelty lies in testing a context-sensitive adaptive non-formal education model tailored to Kazakhstan’s institutional realities. The findings contribute to the global discourse on flexible education strategies and suggest directions for scaling and integrating the model into digital academic ecosystems.

The new and emerging challenges posed by the convergence of cyber threats and socio-political tensions have risen as one of the core formidable threats to the present global security landscape. This paper proposes a hybrid predictive model intended to act against these real-world multidimensional attack vectors. The model integrates cyber threat hunting techniques with socio-political risk assessment methodologies to comprehensively forecast consequent cybersecurity threats to social unrest scenarios. Cyber threat data is collected from sources such as the Offensive Defensive-Intrusion Detection System (OD-IDS2022) and the Aegean Wi-Fi Intrusion Dataset (AWID3), and social terror attack information is gathered from the Global Database of Events, Language, and Tone (GDLET) Project and Armed Conflict Location & Event Data (ACLED) to comprise the bidirectional dataset for the model that contains views from both cyber and socio-political risk landscapes. The model adopts a holistic, robust predictive capability through k-fold cross-validation and feature importance evaluation implementation techniques. This multidisciplinary approach offers a synoptic understanding of emerging and future security threats and enables the execution of proactive measures to secure national and transnational borders.