Bridging the gap: Integrating static and dynamic data for improved permeability modeling and super k zone detection in vuggy reservoirs

Geoenergy Science and Engineering, Год журнала: 2024, Номер 241, С. 213152 - 213152

Опубликована: Июль 22, 2024

Язык: Английский

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Graph convolution networks for social media trolls detection use deep feature extraction

Journal of Cloud Computing Advances Systems and Applications, Год журнала: 2024, Номер 13(1)

Опубликована: Фев. 6, 2024

Abstract This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed machine-learning model capable of detecting images through their embedded text content. Our leverages GloVe word embeddings enhance the model's predictive accuracy. also utilized Graph Convolutional Networks (GCNs) effectively analyze intricate relationships inherent in data. The practical implications our work are significant, despite some limitations performance. While accurately identifies more than half time, it struggles with precision, correctly positive instances less 50% time. Additionally, its ability detect all cases (recall) is limited, capturing only 40% them. F1-score, which measure balance between precision recall, stands at around 0.4, indicating need for further refinement effectiveness. research offers promising step towards effective monitoring moderation platforms.

Язык: Английский

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Deep learning-based instance segmentation architectures in agriculture: A review of the scopes and challenges

Smart Agricultural Technology, Год журнала: 2024, Номер 8, С. 100448 - 100448

Опубликована: Апрель 13, 2024

Deep learning (DL) based instance segmentation has attracted a growing research interest in the scientific community to tackle precision agriculture problems over past few years. However, accurate crop detection and localization complex environments pose significant challenge. Instance is considered as promising DL technique that expands on object perform pixel-wise image address pattern recognition efficiently. In this review, we identify 77 relevant studies DL-based implementations thoroughly investigate them from following perspectives: i) specific architecture employed; ii) data type availability, annotation process pre-processing techniques; iii) performance metrics used; iv) hardware, inference time GPU requirements. Our findings indicate (48 papers) constitutes fundamental task pipeline enable growth monitoring (19 plant health analysis (10 papers). Among them, 6 papers reported robotic manipulation other related automation tasks. Based our can conclude there trend towards two-stage models i.e., Mask R-CNN baseline customized architectures (69 Limitations challenges, such availability of benchmark datasets, open-source codes for semi-automatic tools, technical requirements opportunities future are discussed.

Язык: Английский

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Applications of artificial intelligence (AI) in managing food quality and ensuring global food security

CyTA - Journal of Food, Год журнала: 2024, Номер 22(1)

Опубликована: Сен. 9, 2024

Язык: Английский

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Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues

Journal of Imaging, Год журнала: 2025, Номер 11(2), С. 59 - 59

Опубликована: Фев. 15, 2025

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer and immunology, with object detection, feature extraction, classification, segmentation applications. Advancements in deep learning (DL) research have been a critical factor advancing computer techniques for mining. A significant improvement the accuracy of detection algorithms has achieved result emergence open-source software innovative neural network architectures. Automated now enables extraction quantifiable cellular spatial features from microscope images cells tissues, providing insights into organization various diseases. This review aims to examine latest AI DL mining microscopy images, aid biologists who less background knowledge machine (ML), incorporate ML models focus images.

Язык: Английский

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The Study of Pigments in Cultural Heritage: A Review Using Machine Learning

Heritage, Год журнала: 2024, Номер 7(7), С. 3664 - 3695

Опубликована: Июль 11, 2024

In this review, topic modeling—an unsupervised machine learning tool—is employed to analyze research on pigments in cultural heritage published from 1999–2023. The review answers the following question: What are topics and time trends past three decades analytical study of within (CH) assets? total, 932 articles reviewed, ten identified share these revealed. Each is discussed in-depth elucidate community, purpose tools involved topic. trend analysis shows that dominant over include T1 (the spectroscopic microscopic stratigraphy painted CH assets) T5 (X-ray based techniques for CH, conservation science archaeometry). However, both have experienced a decrease attention favor other more than doubled their share, enabled by new technologies methods imaging spectroscopy processing. These T6 (spectral chemical mapping painting surfaces) T10 technical historical contemporary artists). Implications field conclusion.

Язык: Английский

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Cardiac Repair and Regeneration via Advanced Technology (Preprint)

JMIR Biomedical Engineering, Год журнала: 2025, Номер 10, С. e65366 - e65366

Опубликована: Янв. 8, 2025

Cardiovascular diseases (CVDs) are the leading cause of death globally, and almost one-half all adults in United States have at least one form heart disease. This review focused on advanced technologies, genetic variables CVD, biomaterials used for organ-independent cardiovascular repair systems. A variety implantable wearable devices, including biosensor-equipped stents biocompatible cardiac patches, been developed evaluated. The incorporation those strategies will hold a bright future management CVD clinical practice. study employed widely academic search systems, such as Google Scholar, PubMed, Web Science. Recent progress diagnostic treatment methods against described content, extensively examined. innovative bioengineering, gene delivery, cell biology, artificial intelligence-based technologies that continuously revolutionize biomedical devices regeneration also discussed. novel, balanced, contemporary, query-based method adapted this manuscript defined extent to which an updated literature could efficiently provide research evidence-based, comprehensive applicability CVD. Advanced along with telehealth be essential create efficient stents. proper statistical approaches results from studies model-based risk probability prediction physiological integral monitoring risk. To overcome current obstacles achieve successful therapeutic applications, interdisciplinary collaborative work is essential. Novel their targeted treatments accomplish enhanced health care delivery improved efficacy As articles contain sources state-of-the-art evidence clinicians, these high-quality reviews serve first outline before undertaking studies.

Язык: Английский

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Development of machine learning enhanced low-cost spectrophotometer for pesticide prediction

Measurement, Год журнала: 2025, Номер unknown, С. 116890 - 116890

Опубликована: Фев. 1, 2025

Язык: Английский

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EMAT-Based Crack Detection in Railway Tracks Using Multi-Domain Signal Processing and Scalogram-Driven Deep Learning

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Фев. 14, 2025

This paper presents a novel crack detection approach in railroads using electromagnetic acoustic transducers (EMATs) that can be integrated with multi-domain signal processing techniques and scalogram-driven deep learning approach. In the study nine different scenarios across three critical sections of railway track were investigated. Several useful signals techniques, including time-domain, frequency-domain, Power Spectrum, Periodogram, Welch Method, short-time Fourier transform (STFT), wavelet transform, are implemented to evaluate data acquired through EMAT sensors. Wavelet transformations applied proposed segments generate scalogram images, which used as an input model training. When results compared conventional machine classifiers, performs better, exhibiting higher accuracy identifying types cracks from images. The demonstrate EMAT-based fracture identification, advanced processing, greatly enhance inspection safety, even though system currently processes batches rather than real time. Future work will focus on real-time acquisition further optimization architecture.

Язык: Английский

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AI-driven UAV system for autonomous vehicle tracking and license plate recognition

Open Engineering, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 1, 2025

Abstract The integration of Artificial Intelligence (AI) with image processing and autonomous flight capabilities in Unmanned Aerial Vehicles (UAVs) represents a significant advancement modern surveillance tracking systems. This research explores novel method for locating vehicles pre-identified license plate numbers through an AI-enhanced framework. proposed system captures vehicle details stores them subsequent comparison. Autonomous UAVs are deployed within predefined area to capture high-resolution images plates, which then processed analysed using advanced AI algorithms designed optical character recognition machine learning. Recognized matched against pre-stored entries real-time. Upon identification match, the accurately determines displays vehicle’s location, providing precise geospatial data. approach demonstrates high precision efficiency tracking, significantly improving upon conventional techniques, often rely on manual monitoring static camera setups. AI-driven not only enhances accuracy but also reduces time human resources required. study broader implications potential applications this across various sectors. In law enforcement, it enables real-time stolen or suspects. traffic management, assists managing flow enforcing parking regulations. security monitoring, perimeter by identifying unauthorized restricted areas. underscores system’s robustness adaptability practical applications, marking step forward field automated tracking.

Язык: Английский

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