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AGRICULTURAL FIELD BOUNDARY DETECTION THROUGH INTEGRATION OF "SIMPLE NON-ITERATIVE CLUSTERING (SNIC) SUPER PIXELS" AND "CANNY EDGE DETECTION METHOD"

arXiv

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arXiv 2025年

作者： Gayibov, Artughrul Department of Information Technologies and Programming Baku Engineering University Hasan Aliyev str. 120 BakuAZ0101 Azerbaijan

Efficient use of cultivated areas is a necessary factor for sustainable development of agriculture and ensuring food security. Along with the rapid development of satellite technologies in developed countries, new methods are being searched for accurate and operational identification of cultivated areas. In this context, identification of cropland boundaries based on spectral analysis of data obtained from satellite images is considered one of the most optimal and accurate methods in modern agriculture. This article proposes a new approach to determine the suitability and green index of cultivated areas using satellite data obtained through the "Google Earth Engine" (GEE) platform. In this approach, two powerful algorithms, "SNIC (Simple Non-Iterative Clustering) Super Pixels" and "Canny Edge Detection Method", are combined. The SNIC algorithm combines pixels in a satellite image into larger regions (super pixels) with similar characteristics, thereby providing better image analysis. The Canny Edge Detection Method detects sharp changes (edges) in the image to determine the precise boundaries of agricultural fields. This study, carried out using high-resolution multispectral data from the Sentinel-2 satellite and the Google Earth Engine JavaScript API, has shown that the proposed method is effective in accurately and reliably classifying randomly selected agricultural fields. The combined use of these two tools allows for more accurate determination of the boundaries of agricultural fields by minimizing the effects of outliers in satellite images. As a result, more accurate and reliable maps can be created for agricultural monitoring and resource management over large areas based on the obtained data. By expanding the application capabilities of cloud-based platforms and artificial intelligence methods in the agricultural field, it can contribute to the establishment of more efficient and sustainable agricultural *** Codes 68U10, 62H35 © 2025, CC BY.

关键词： Resource allocation

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A lightweight attention-driven YOLOv5m model for improved brain tumor detection

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Computers in Biology and Medicine 2025年 188卷 109893-109893页

作者： Muksimova, Shakhnoza Umirzakova, Sabina Mardieva, Sevara Iskhakova, Nargiza Sultanov, Murodjon Cho, Young Im Department of Computer Engineering Gachon University Sujeong-gu Seongnam-si461-701 Korea Republic of Department of Information Systems and Technologies of the Tashkent State University of Economic Tashkent100066 Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent100200 Uzbekistan

Brain tumors are regarded as one of the most lethal, devastating, and aggressive diseases, significantly reducing the life expectancy of affected individuals. For this reason, in pursuit of advancing brain tumor diagnostics, this study introduces a significant enhancement to the YOLOv5m model by integrating an Enhanced Spatial Attention (ESA) layer, tailored specifically for the analysis of magnetic resonance imaging (MRI) brain scans. Traditional brain tumor detection methods, heavily reliant on expert interpretation of MRI, are fraught with challenges such as high variability and the risk of human error. Our innovative approach leverages the ESA layer to acutely focus on salient features, significantly improving the method ability to differentiate between common classes of brain tumors—meningioma, pituitary, and glioma tumors. By processing spatial features with enhanced precision, the model minimizes false positives and maximizes detection reliability. Validated against a comprehensive dataset of 3064 T1-weighted contrast-enhanced MRI images from 233 patients, our modified YOLOv5m architecture demonstrates superior performance metrics compared to the standard model, highlighting its potential as a robust tool in clinical applications for automated and precise brain tumor diagnosis. © 2025 The Authors

关键词： Dynamic contrast enhanced MRI

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Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI

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Computers in Biology and Medicine 2025年 190卷 110095-110095页

作者： Muksimova, Shakhnoza Umirzakova, Sabina Iskhakova, Nargiza Khaitov, Aziz Cho, Young Im Department of Computer Engineering Gachon University Sujeong-gu Gyeonggi-do Seongnam-si461-701 Korea Republic of Department of Systematic and Practical Programming Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent100200 Uzbekistan Republican Scientific and Methodological Center for the Development of Education of the Republic of Uzbekistan Uzbekistan

This paper introduces a novel convolutional neural network model with an attention mechanism to advance Alzheimer disease (AD) classification using Magnetic Resonance Imaging (MRI). The model architecture is meticulously crafted to enhance feature extraction and selectively focus on crucial regions within brain images, thereby improving diagnostic accuracy. A unique component, the MRI Segmentation Block (MSB), is introduced to manage the skull stripping task effectively, highlighting the model ability to learn from complex, multilayered information. We have incorporated a detailed experimental evaluation of the MSB, demonstrating its superior performance in cranial debridement tasks compared to existing methods. The experiments involved a range of MRI scans, assessing the MSB's accuracy through metrics like the Dice Coefficient and Jaccard Index against ground truth annotations by expert radiologists. The results substantiate the MSB's effectiveness, setting a new benchmark for precision in medical imaging diagnostics. The proposed method integrates densely connected neural networks with a connection-wise attention model to extract multiscale features from MRI scans. Furthermore, the attention mechanism is fine-tuned to emphasize salient features significantly associated with various stages of Alzheimer's disease, thereby setting a new benchmark for precision in medical imaging diagnostics. Extensive experiments on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset demonstrate the superiority of our method over traditional and contemporary approaches, with our model achieving high accuracy and computational efficiency. This makes it suitable for clinical applications where resources are limited. This study represents a significant advancement in the diagnostic process for AD, with potential implications for improving patient outcomes in clinical settings. © 2025 The Authors

关键词： Convolutional neural networks

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Research on building scientific data ontology

Research on building scientific data ontology

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4th International Conference on Developments in eSystems Engineering, DeSE 2011

作者： Goranova, Mariana Shishedjiev, Bogdan Georgieva, Juliana Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9781457721861

When dealing with complex systems design and application the notion of ontologies helps the process of application integration. To solve the problems with discover, interpret and reuse data we have to recognize ontology as a concept that formalizes the management and integration of information, services and processes. In this paper we present our work towards the development and evaluation of scientific data ontology that helps the system integration architect achieve interoperability between different systems. © 2011 IEEE.

关键词： Ontology

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Data security and validation framework for a scientific data processing SOA based system

Data security and validation framework for a scientific data...

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4th International Conference on Developments in eSystems Engineering, DeSE 2011

作者： Gancheva, Veska Programming and Computer Technologies Department Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9781457721861

A data security and validation framework of a SOA based system for management, storage, processing and visualization of data obtained from scientific experiments is proposed in this paper. The framework covers the three levels of data security: authorized user access;data encryption and data validation. To ensure authorized user access three access levels are provided: ownership, distribution rights and read rights. To ensure the data encryption an asymmetric 128-bit encrypting method based on public and private key is implemented. A universal approach for complete data validation has been suggested and implemented as web service. Data validation service allows validating the input data processed by each of the modules in the system. A declarative language defining the data validation and their interpretation based on XML standard is specified, by which precise rules in XML format are established and simultaneously with a library that supports this grammar is performed. In the language structures are included elements describing the data, rules and logic, which have to satisfy the input data. The actual definition is supported by a definition repository XML file and allows adding additional field types or complex validation logic as needed and easy modification of the validation rules. © 2011 IEEE.

关键词： Web services

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Automatic conversion of scientific data into canonical format

Automatic conversion of scientific data into canonical forma...

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IEEE EUROCON-International Conference on Computer As A Tool

作者： Goranova, Mariana Shishedjiev, Bogdan Georgieva, Juliana Achev, Velin Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9781424474868

The purpose of the automatic conversion of scientific data into canonical format is to provide a link between raw representation of data and database schemas. On the basis of these concepts Web services are developed for automatic XML description. They describe structurally and semantically scientific data and the conversion of the raw data layout description into canonical form based on data semantics. The canonical form looks like a relational data model where efficient methods for storage and retrieval of scientific data have been used. This paper describes the implementation of the converter compiler of raw scientific data layout description into canonical format. Using canonical XML form we have achieved better standardization of the input for data manipulation and visualization routines and minimization of their number. © 2011 IEEE.

关键词： XML

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Estimation of Energy Performance Classes of Concrete Structures with Artificial Intelligence Models 3

Estimation of Energy Performance Classes of Concrete Structu...

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3rd International Conference on High Technology for Sustainable Development, HiTech 2020

作者： Can, Hamit Minkovska, Daniela Stoyanova, Lyudmila Technical University of Sofia Department of Programming and Computer Technologies Sofia Bulgaria

ISBN: (纸本)9781728186511

The construction sector is one of the sectors that make energy efficiency a must. In almost every field related to the construction sector, there is a need for studies in which high performance can be achieved by including artificial intelligence in energy. In this study, energy class models, which are an area of artificial intelligence, which show the efficiency level of energy systems that determine the energy performance of buildings, are examined. © 2020 IEEE.

关键词： Energy efficiency

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Using Natural Language Processing for Quality Assurance Purposes in Higher Education

Using Natural Language Processing for Quality Assurance Purp...

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2021 International Conference on High Technology for Sustainable Development, HiTech 2021

作者： Ujkani, Berat Minkovska, Daniela Stoyanova, Lyudmila Technical University of Sofia Department of Programming and Computer Technologies Sofia Bulgaria

ISBN: (纸本)9781665448734

Nowadays, quality is the most important attribute that creates value. It is considered as the competitive advantage of private and public organizations by which they differentiate their products or services. Quality assurance in higher education is concerned more than ever before. This paper presents a system intended to facilitate some aspects of the quality assurance process in the universities by helping both the quality assurance officers as well as lecturers. The system accepts as an input the learning outcomes of syllabuses and program curricula and based on some specific rules performs consistency checks between them, assuring that program delivers to students what it actually promises. © 2021 IEEE.

关键词： Curricula

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XML-based language for specific scientific data description

XML-based language for specific scientific data description

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5th International Conference on Internet and Web Applications and Services, ICIW 2010

作者： Shishedjiev, Bogdan Goranova, Mariana Georgieva, Juliana Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9780769540221

The scientific computing manages massive amounts of very large data sets. The development of description of the format, structure and semantic content of data is essential for the automatic manipulation of data. The aim of this paper is to define an XML-based language for describing the structure, semantic and annotation of scientific data. The XML Schema and the Document Type Definitions (DTDs) that implement the abstract format are provided. The proposed language describes the structure, semantic and annotation of the scientific data from experiments and simulations. © 2010 IEEE.

关键词： Data description

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On Algebras of Hausdorff Operators on the Real Line

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arXiv 2023年第2期271卷 109-114页

作者： Liflyand, E. Mirotin, A. Department of Mathematics Bar-Ilan University Ramat-Gan52900 Israel Department of Mathematics and Programming Technologies Francisk Skorina Gomel State University Gomel Belarus

关键词： Fourier transforms

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