Climate change is a substantial threat to worldwide food security, affecting the supply, stability, accessibility, and quality of food. This study aimed to explore the impact of climate change on household food security of farming communities in dry zones in Sri Lanka, focusing on water resource limitations and agricultural productivity, using a qualitative case study method with 13 cases. The impact of climate change on farming and food security was identified under the key themes of food production and yield, income and economic stability, water resources and management, food availability and access, nutrition quality, and dietary diversity. The findings revealed that climate change can indirectly affect food security by impacting household and personal incomes. It also influences health, access to clean water, and the ability to utilize food effectively. Such climatic changes significantly impact household food security and distinctly affect nutrition quality and dietary diversity, which are identifiable as the primary food security elements. This study suggests moving other income sources to enhance the economic stability of farming households, adopting new farming techniques, organizing government assistance programs, and establishing social safety nets such as food aid programs and financial support for affected households. Such activities will help to address the decline of yield production that is caused by climate change, and will mitigate the effect that climate change has on household food security. It further emphasizes the need to combine modern policy interventions and the existing domestic adaptation framework. This investigation employs a qualitative research method to explore how communities experience actual climate change effects, including water issues and farm failures. It effectively captures and contributes detailed knowledge to the current research on this subject.

Zero-day intrusion detection is still a difficult task because of the difference between high laboratory precision and real-time deployability under strict operational constraints. This paper proposes a lightweight two-stage cascade architecture that is specifically designed for CPU-only environments and strict zero-day evaluation. The proposed architecture only uses statistical and flow-level metadata attributes, which are independent of payload analysis, to ensure compatibility with encrypted traffic. The first stage of the proposed architecture is precision oriented to detect potentially malicious traffic with a low decision threshold, and the second stage is precision oriented to enhance classification and remove false positives. To avoid optimistic bias, a strict attack-type separation protocol is employed, where testing attack types are strictly prohibited from training. The proposed method is tested on three benchmark datasets: CSIC 2012 (HTTP level), UNSW-NB15 (intra-domain), and CSE-CIC-IDS2018 (cross-domain). The experimental results show the excellent intra-domain zero-day detection capability (up to 94.81% accuracy with 0.50% FPR), controllable performance degradation in the cross-domain setting (80.53% accuracy with near-zero FPR), and extremely low FP rates on all datasets. The system provides microsecond-level inference latency (0.002–0.006 ms), a throughput of up to 470,000 requests per second, and memory usage below 6.2 MB without GPU support. These results confirm the significance of architectural optimization and thorough evaluation in building efficient zero-day detection systems.

Background: Accurate and early detection of hematological disorders from microscopic peripheral blood smear images remains a technically challenging task due to inherent imaging limitations, including noise contamination, low contrast, staining variability, and significant cellular overlap. Conventional deep learning-based object detection frameworks often exhibit limited robustness under such conditions and demonstrate reduced sensitivity to small-scale morphological structures, particularly platelets and abnormal cell variants. Methods: To address these challenges, this study proposes a hybrid detection framework that integrates a fuzzy logic-driven image preprocessing module with the YOLOv11 object detection architecture. The proposed preprocessing pipeline employs adaptive fuzzy membership functions to normalize pixel intensity distributions, suppress high-frequency noise, and enhance edge-defined cellular boundaries. This transformation produces a structurally optimized feature representation, improving downstream feature extraction and localization performance. The proposed framework was evaluated on a curated dataset of 3000 annotated microscopic blood smear images spanning five hematological classes. Results: Experimental results show that the fuzzy logic module improves mAP@0.5 by +3.4% and mAP@0.5:0.95 by +3.6%, confirming its effectiveness in enhancing both classification and localization accuracy. Conclusions: These findings demonstrate the robustness and practical applicability of the proposed hybrid approach under challenging imaging conditions.

This article presents a simple and understandable approach to the automatic assessment of the severity of late blight on tomato leaves. We collect our own dataset of 5245 RGB images of healthy and diseased tomato leaves and determine five ordinal classes: healthy (0%) and four infection levels (0.1–10%, 11–25%, 26–50%, and ≥51% of the affected area). Each image is segmented using the global definition of the Otsu threshold, followed by morphological purification, after which seven textural and geometric characteristics are extracted from the contours of the lesion: contrast, number of contours, average and standard deviation of the contour area, average and standard deviation of the contour perimeter, and average area-to-perimeter ratio. All characteristics are normalized and used as input data for the XGBoost classifier. The dataset is randomly split into 80% training and 20% test images, resulting in an independent test set of 1049 images. In this test set, the proposed model provides an overall accuracy of 0.93 and an F1 macro score of 0.93 points, while for each F1 class, it varies from 0.90 to 0.97. The confusion matrix shows a stable difference between neighboring severity levels, while the analysis of the importance of the features confirms the relevance of contour descriptors for characterizing the size and shape of the lesion. This method only runs on a central processor, requires a small amount of memory, and outputs interpretable output data, making it suitable for use in greenhouses and farms with limited computing resources. We also discuss the limitations associated with the boundaries between neighboring classes and the potential shift in the subject area, and we outline directions for expanding the approach to multi-sheet scenes and explicit ordinal loss functions.

The agro-industrial complex of Kazakhstan faces increasing pressure to improve the efficiency of marketing activities under conditions of limited budgets, regional heterogeneity, seasonal demand, and uneven digital infrastructure. Traditional marketing planning methods often rely on expert judgment or single-objective optimization and therefore cannot adequately address the conflicting objectives of maximizing marketing effectiveness, expanding audience coverage, and minimizing campaign expenditure. This study proposes a hybrid decision-support framework that combines the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with machine learning-based clustering for multi-objective marketing budget optimization in the agro-industrial sector. The model considers five major promotion channels: digital media, television, radio, print media, and events. NSGA-II is used to generate Pareto-optimal budget allocation strategies, while K-means clustering classifies the resulting solutions into interpretable strategy groups. The framework was evaluated using synthetic data and enterprise-level data reflecting marketing conditions in Kazakhstan. Results show that strategies combining digital media and television achieve the highest marketing effectiveness and audience coverage, although they require greater campaign expenditure. Radio and print media remain relevant for cost-sensitive strategies, particularly for enterprises with limited resources. Cluster analysis identified three main strategy groups: cost-efficient, balanced, and high-performance configurations. The findings confirm that marketing budget allocation in the agro-industrial sector should be treated as a multi-objective optimization problem rather than a single-criterion decision. The proposed framework provides both computational optimization and managerial interpretability, supporting data-driven, adaptive, and resource-aware marketing planning for agro-industrial enterprises in Kazakhstan.

Recognizing facial emotions automatically from images/videos (FER) still represents a difficult problem for emotion computing, mainly due to variations in the face pose, lighting, occlusion, facial features, and expression intensity in the wild. Recent CNN–Transformer-based hybrid models like POSTER have leveraged local feature learning, landmark guidance, and global dependency modeling to achieve strong performance. Yet these methods give the main focus to spatial and contextual representations while not really going deep into adaptive channel-wise feature importance over multi-scale representations. As different feature channels represent emotions in varying degrees, it is likely that by treating all feature channels equally, one would limit the ability of the learned features to discriminate effectively. To overcome this weakness, this article presents a ResNet-18–Transformer landmark-guided module called SE-POSTER that fuses lightweight Squeeze-and-Excitation (SE) attention modules into the multi-scale feature pyramid of the baseline POSTER architecture. The proposed method carries out feature channel recalibration adaptively at the level of features before Transformer-based global attention modeling, thus allowing the network to focus on emotionally informative feature channels and suppress less relevant responses. The inclusion of SE attention in the network enhances fine, mid, and global levels of feature representations at a very low cost in terms of computation. On the basis of the RAF-DB, FERPlus, and AffectNet datasets, enormous experiments prove that the SE-POSTER framework proposed is capable of steadily boosting recognition accuracy relative to the baseline POSTER and several state-of-the-art FER methods. Especially, the proposed model delivers 92.78% accuracy on RAF-DB while it also shows better robustness and generalization capability under difficult real-world conditions. Moreover, additional ablation studies reveal that multi-level channel recalibration is effective in improving discriminative emotional feature learning.