This paper introduces an enhanced YOLOv11 model for accurate and efficient weed detection in precision agriculture. Our improvements to YOLOv11n include integrating SPD-Conv for faster speed and better multi-scale fusion, embedding the SCSA attention mechanism in Bottlenecks to mitigate occlusion-related misses, and utilizing the CIoU loss for an improved precision-recall balance. Leveraging the CottonWeedDet12 dataset augmented with random transformations, our model achieves a 1% higher mAP than YOLOv11n and outperforms counterparts like YOLOv5/7/8. It demonstrates robust performance across varying lighting, occlusion, and complex soil conditions, offering a viable solution for smart farming applications.

The paper provides an overview of the anomaly detection techniques to address the ever-evolving landscape of code vulnerabilities. Main objective is to provide a comprehensive understanding of the various methodologies, algorithms, and frameworks that have been developed to enhance the security and reliability of software systems. In paper determines early research in anomaly detection techniques from 2019 and followed methods, approaches and frameworks until recent times. According to research are defined challenges such as high computational costs, false positives, and the need for large, labeled datasets persist. The work specifies ML methods and calculates classic classification metrics such as accuracy, F1 score, precision, recall, geometric mean, etc. This review underscores the importance of continuous advancements in anomaly detection techniques to address the ever-evolving landscape of code vulnerabilities.

This paper examines the combination of traditional banking credit assessment techniques with contemporary internal sales accounting systems in Kazakhstan, aiming to augment the precision and resilience of financial assessments pertaining to SMEs. The proposed model consists of two discrete components: a traditional credit scoring module that employs logistic regression and a supplementary sales analytics module that leverages ensemble machine learning methodologies — random forests and gradient boosting algorithms. The outputs generated by these components are amalgamated through an ensemble strategy, where optimal weighting coefficients are ascertained via cross-validation. An empirical analysis was conducted on a dataset encompassing 41,000 SME records from a prominent Kazakhstan bank alongside daily transactional sales data from 150 SMEs gathered between the years 2021 and 2024. The integrated hybrid model demonstrated a statistically meaningful enhancement in predictive efficacy, as evidenced by an increase in the area under the ROC curve from 0.76 to 0.87 and a decrease in mean squared error from 0.12 to 0.08 relative to the traditional methodology. The investigation delves into the transformative influence of digitalization on innovation within SMEs, elucidating that improved real-time data integration not only sharpens risk assessment processes but also promotes adaptive lending strategies and operational efficiencies.

This paper introduces a hybrid credit assessment methodology tailored specifically to the unique financial, informational, and operational challenges faced by importers. Importers operate at the intersection of cross-border financial risk, fluctuating supply chains, and opaque transactional histories - factors that challenge conventional credit models. While banks rely on structured financial records and established credit histories, P2P platforms harness alternative data and decentralized trust mechanisms such a peer feedback and real-time behavioral signals. Rather than merely merging technologies, the proposed approach integrates differing capabilities: institutional data interpretation dynamical risk modeling, and platform-based investor participation. This synthesis enables a more adaptive, inclusive, and context-aware assessment process. Core components include continuous data validation, alternative trade-data integration, machine learning-driven decision support, and modular feedback loops from lender communities. The methodology is evaluated through a structured literature review and illustrative case applications. By focusing on the informational volatility and operational complexity unique to import-driven businesses, this research contributes a targeted solution to a global financing gap - advancing both credit innovation and trade accessibility.

With the rapid development of big data and internet technology, big data financial platform companies collect and organize massive data through their own platforms, improve credit scoring parameters, and use machine learning methods to conduct comprehensive and scientific credit scoring assessments. Thus, banks in the construction of credit scoring face big problems. Based on the limitations of the existing system and methods of personal credit score, it is necessary to study personal credit score based on machine learning methods, improve the parameters and scoring system of personal credit score, clarify data collection channels, and use dynamic desensitization technology to desensitize data, LOF test method to test outliers' data and a random forest method to complete missing data values. You then use the gradient boosting decision tree method to view the important indicators, process the tested indicators with a scorecard model based on logistic regression, and derive a personal credit score. Finally, the model is tested with a BP neural network and the model is used to predict the level of personal credit. The study shows that machine learning can further improve the accuracy of individuals' credit ratings and provide the scientific basis and reference for credit ratings of commercial banks.

The object of the study is the text classification and semantic search tailored to the unique linguistic features of the Kazakh language. The research addresses the challenge of improving the accuracy, relevance, and efficiency of semantic search. This study focuses on improving semantic search for the Kazakh language by analyzing computational models tailored to its unique linguistic features, such as agglutinative morphology and rich inflectional systems. The research compares traditional rule-based approaches and advanced transformer architectures, including fine-tuned models like RoBERTa, for their ability to handle semantic nuances, contextual relationships, and user intent. The results reveal that fine-tuned transformer models achieved significant advancements, with the RoBERTa model attaining a Precision@10 of 89.4 %, a Mean Reciprocal Rank (MRR) of 85.6 %, and an F1-Score of 88.0 %. Additionally, the semantic search system developed in this study demonstrated a precision of 88.4 %, recall of 87.6 %, and an F1-score of 88.0 % on a domain-specific Kazakh dataset. Key to these improvements were innovations in preprocessing pipelines, including custom tokenization and lemmatization tailored to Kazakh's agglutinative morphology, and the integration of contextual embeddings to resolve issues such as synonymy and homonymy. Computational efficiency was enhanced through resource optimization techniques, enabling the deployment of these advanced models in constrained environments. These findings underscore the potential of tailored transformer models to bridge the gap in semantic search capabilities for underrepresented languages like Kazakh, advancing the inclusivity of natural language processing technologies

A composite intelligence scoring framework for identifying high-potential individuals using multi-metric predictive models

Background: The modern credit card system is critical, but it has not been fully examined to meet the unique financial needs of a constantly changing number of manufacturers and importers. Methods: An intelligent credit card system integrates the features of artificial intelligence and blockchain technology. The decentralized and unchangeable ledger of the Blockchain technology significantly reduces the risk of fraud while maintaining real-time transaction recording. On the other hand, the capabilities of AI-driven credit assessment algorithms enable more precise, effective, and customized credit choices that are specifically tailored to meet the unique financial profiles of manufacturers and importers. Results: Several metrics, including predictive credit risk, fraud detection, credit assessment accuracy, default rate comparison, loan approval rate comparison, and other important metrics affecting the credit card system, have been investigated to determine the effectiveness of modern credit card systems when using Blockchain technology and AI. Conclusion: The study of developing an intelligent scoring system for crediting manufacturers and importers of goods in Industry 4.0 can be enhanced by incorporating user adoption. The changing legislation and increasing security threats necessitate ongoing monitoring. Scalability difficulties can be handled by detailed planning that focuses on integration, data migration, and change management. The research may potentially increase operational efficiency in the manufacturing and importing industries.

The results of electrical exploration carried out using the method of magnetotelluric sounding at a deposit in Western Kazakhstan are presented. Based on processing and interpretation of the electrometric data, a digital geoelectric model of the exploration area is developed and a geoelectric section of the test profile is constructed. It is shown that MT soundings, along with a seismic survey and well-log data, can be effectively used to define a geological structure and to identify prospective oil-and-gas bearing segments in Mesozoic terrigeneous-sedimentary complexes.

The article discusses the results of interpretation of gravity survey data in the southern part of the Ustyurt region to identify zones and areas heterogeneous in terms of density properties of rocks. The study relates the distribution of anomalous gravity field with areas potentially productive for hydrocarbon accumulations. Gravity field transforms, Euler points, magnetic survey data, airborne gamma-ray spectrometry and CDP-2D seismic data were used to determine structural features and prospects of oil-and-gas bearing local structures at the Shakhpakhty step.