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Numerical Solution to a Problem of Optimizing Placement and Flow Rates of Wells 2nd

Numerical Solution to a Problem of Optimizing Placement and ...

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2nd International Conference on information Technologies and Their Applications

作者： Bagirov, Arzu Gunkina, Tatiana Handzel, Alexander Inst Control Syst Baku Azerbaijan North Caucasus Fed Univ Stavropol Russia

ISBN: (纸本)9783031734199;9783031734205

The paper proposes an approach to the numerical solution of the problem of optimizing wells' placement and the values of their flow rates. The selection of rational placement of wells is the first and extremely important task that oil and gas companies face when designing the development of hydrocarbon fields. Typically, standard patterns are used for well placement, in which wells are located at the nodal points of various regular grids. The method proposed in this paper for determining the optimal placement of wells and their flow rates is based on considering an oil reservoir as a dynamic system with distributed parameters and on application of the methods for optimal control of such systems. The optimization goal is to achieve a uniform distribution of reservoir pressure over the area of the oil deposit after withdrawing the planned volume of oil from it. Fulfillment of this criterion ensures, in particular, a uniform rise of bottom water and prevents early water flooding of production wells. The state of the reservoir is described by the equation of non-stationary two-dimensional filtration of weakly compressible oil. Thus, the problem under consideration is a problem of optimal control of distributed systems, the solution of which is carried out by reducing the original problem to a discrete problem for which the gradient of the objective functional is determined, which allows to use standard first-order optimization methods. The paper presents the results of numerical experiments and their analysis.

关键词： Oil Field Filtration Placement of Wells Flow Rate Distributed System Optimal Control

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Uncovering the ANPR Performance Gap: A Commercial Systems Analysis 5th

Uncovering the ANPR Performance Gap: A Commercial Systems An...

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5th Southern African Conference for Artificial Intelligence Research

作者： Zandamela, Frank Seletani, Rofhiwa Malatjie, Patrick Kunene, Dumisani Nana, Muhammad Ahmed Sekopa, Teboho CSIR Def & Secur ZA-0001 Pretoria South Africa

ISBN: (纸本)9783031782541;9783031782558

Automatic number plate recognition (ANPR) systems play a vital role in global security and crime prevention. However, despite their importance, there is a notable lack of research assessing commercial off-the-shelf ANPR systems. Given that ANPR systems are not universally applicable due to regional variations in number plates, performance varies per region. A recent two-year study of a commercial ANPR system revealed a concerning average accuracy of 70%. This falls significantly short of the global average of 90%, compromising the reliability of the ANPR results. This study aims to address this knowledge gap by investigating factors that influence the performance of ANPR systems and comparing the effectiveness of commercial off-the-shelf solutions on South African vehicle plates. Four cloud-based ANPR solutions and an ANPR camera were assessed with a custom dataset. The custom dataset contains vehicle plates from different provinces and a government department. The study revealed that the camera viewing angle significantly affected the accuracy of the deployed ANPR system. Cloud-based ANPR APIs outperformed the ANPR camera system by 6.67%. In addition, most ANPR systems have difficulty detecting and correctly recognising number plate characters when there are multiple vehicles in the image. These findings underscore the need for region-specific optimisation and more comprehensive evaluation frameworks to enhance the reliability and accuracy of commercial ANPR systems in diverse real-world scenarios.

关键词： Automatic number plate recognition Automatic number plate recognition Commercial systems Performance evaluation Optical Character Recognition

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An RNN-LSTM Approach for Algerian Accent Identification 4th

An RNN-LSTM Approach for Algerian Accent Identification

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Zergat, Kawthar Yasmine Selouani, Sid Ahmed Amrouche, Abderrahmane Univ Sci & Technol Houari Boumediene Speech Com & Signal Proc Lab POB 32 Algiers 16111 Algeria Univ Moncton Informat Management Dept Campus Shippagan Shippegan NB 8S IP6 Canada

ISBN: (纸本)9783031821554;9783031821561

Dialect identification is exploited as a valuable tool in forensic investigations, aiding in geolocating suspects, identifying suspects from recordings, and authenticating recordings. Additionally, it assists investigators in narrowing down the search space and facilitating the recognition process. This paper aims to assess fine-grained Algerian dialect identification. Algerian speakers often engage in code-switching, seamlessly alternating between Modern Standard Arabic (MSA), Algerian Arabic, and even French within the same conversation. This linguistic fluidity adds further complexity to Algerian dialects. In this paper, we introduce Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) architecture to accurately and efficiently identify the four main Algerian accents. The classifier is trained using a fine-grained Algerian corpus, with a feature vector composed of 39 Mel-Frequency Cepstral Coefficients (MFCCs) extracted from audio signals. Experimental results demonstrate that the proposed RNN-LSTM classifier achieves interesting results with a precision of 0.79, 0.8, 0.79 and 0.79, recall of 0.83, 0.79, 0.82 and 0.69 and F1-score of 0.82, 0.79, 0.81 and 0.73 for the Central accent, Eastern accent, Western accent and Southern accent respectively. A global correct identification rate is about 79%.

关键词： Algerian corpus Dialect and accent recognition RNN-LSTM MFCC Forensic system

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GAN-Enhanced Deep Learning Approach for Forecasting the Potentially Toxic Cyanobacteria in Dams 4th

GAN-Enhanced Deep Learning Approach for Forecasting the Pote...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Amara, Nour Djihane Dendani, Nadjette Berrezzek, Amira Khemissa, Hana Saoudi, Amel Azizi, Nabiha Badji Mokhtar Univ Dept Comp Sci Annaba Algeria Badji Mokhtar Univ Dept Biochem Annaba Algeria

ISBN: (纸本)9783031821523;9783031821530

This research delves into the phenomenon of Cyanobacterial Harmful Algal Blooms (CyanoHABs), which are caused by cyanobacteria and pose significant threats to freshwater ecosystems such as lakes, rivers, reservoirs, and dams, due to the production of cyanotoxins that endanger human and animal health. The primary objective of this study is to protect public health, water resources and to improve efficient bloom management techniques using advanced Artificial Intelligence (AI) models to forecast the onset of CyanoHABs in freshwater ecosystems. Our approach employs a Generative Adversarial Network (GAN)enhanced deep learning (DL) model in comparison with traditional regression methods such as Multi-Linear Regression (MLR), Support Vector Regression (SVR) refined through hyperparameter tuning, Decision Tree Regression (DTR), and Random Forest Regression (RFR). Our study demonstrates the effectiveness of GANs in synthesizing new data points that are diverse and realistic, thereby enriching existing datasets and enhancing the performance of DL models. Notably, the outstanding forecasting accuracy of the GAN-enhanced 1Dimensional Convolutional Neural Network (1D CNN) model, achieving an impressive R-squared (R2) value of 0.98, underscores its efficacy in predicting occurrences of CyanoHABs.

关键词： Toxins Cyanobacteria Deep Learning 1Dimensional Convolutional Neural Network Generative Adversarial Network

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Enhancing Irrigation Efficiency with a Unified Stochastic Decision Tree Model: Predictive Analysis of Stem Water Potential in Almond and Pistachio Orchards 8th

Enhancing Irrigation Efficiency with a Unified Stochastic De...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Alizadeh, Azar Mortazavi, Mehrad Farajijalal, Mohsen Toudeshki, Arash Ehsani, Reza Singhal, Mukesh Univ Calif Merced Dept Elect Engn & Comp Sci Merced CA 95343 USA Univ Calif Merced Dept Mech Engn Merced CA USA

ISBN: (纸本)9783031856273;9783031856280

Stem Water Potential (SWP) is the standard method for assessing water stress and irrigation scheduling in tree crops. This method is time-consuming and labor-intensive, limiting data collection to only a few trees in the orchard. To find an alternative approach that predicts water stress in every tree in the orchard, we implemented a novel Stochastic Decision Tree (SDT) method, utilizing remote sensing and weather data to predict SWP in almond and pistachio orchards. The input data for our model included various vegetative indices such as NDVI, GNDVI, OSAVI, LCI, and NDRE, as well as local weather parameters, such as temperature (Ta), relative humidity (RH), air pressure (P), Vapour Pressure Deficit (VPD), and the Water Stress Index (WSI). Our results indicate that the SDT model achieves a prediction accuracy of nearly 94%, Outperforming Random Forest (RF), support vector machine (SVM), and the k-nearest neighbor (KNN) algorithms. We investigated various combinations of collected data under different scenarios to improve the impact of sensor-derived data from pistachio and almond orchards and enhance the accuracy of SWP predictions using an SDT model. Our findings suggest that a data-driven model utilizing cost-effectively collected data can predict water stress. The successful development of a universal model enhances the accuracy of SWP predictions. Moreover, its adaptability and effectiveness allow it to be utilized for different orchards, making it highly applicable to real-world agricultural scenarios.

关键词： Stem water potential Stochastic decision tree Water stress level

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A Hybrid Machine Learning Approach for Accurate Milk Quality Classification 4th

A Hybrid Machine Learning Approach for Accurate Milk Quality...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Mansi Chauhan, Sneha Patnaik, Monalika Anand, Rishika Sabharwal, Aditi Reddy, S. R. N. Indira Gandhi Delhi Tech Univ Women CSE New Delhi 110006 India

ISBN: (纸本)9783031837951;9783031837968

Milk quality plays a critical role in the dairy industry, impacting not only consumer health but also the overall economic efficiency of dairy production. Ensuring high-quality milk is essential for producing safe dairy products, maintain- ing shelf life, and adhering to regulatory standards. However, determining milk quality based on various parameters such as fat content, pH levels, and microbial content poses significant challenges. Traditional methods of testing milk quality are often time-consuming and prone to human error. In this paper, a machine learning technique has been presented, which, improves the efficiency of classification of milk quality. We utilized several models to analyze a dataset that includes important milk quality parameters. While individual models achieved high accuracies, our hybrid model surpassed them with an impressive 100% accuracy. The confusion matrix and classification report show highly accurate predictions across all categories (high, medium, low). This method not only tackles the difficulties associated with manual testing but also offers a dependable and scalable solution for assessing milk quality in real-time. This benefits both producers and consumers by guaranteeing safer and higher-quality milk.

关键词： Random Forest K-Nearest Neighbors Decision Tree Support Vector Classifier Milk Quality

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Unsupervised Analysis of Alzheimer's Disease Signatures Using 3D Deformable Autoencoders 1st

Unsupervised Analysis of Alzheimer's Disease Signatures Usin...

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1st MICCAI Meets Africa Workshop

作者： Avci, Mehmet Yigit Chan, Emily Zimmer, Veronika Rueckert, Daniel Wiestler, Benedikt Schnabel, Julia A. Bercea, Cosmin I. Tech Univ Munich Munich Germany Helmholtz Ctr Munich Munich Germany Klinikum Rechts Der Isar Munich Germany Kings Coll London London England Imperial Coll London London England

ISBN: (纸本)9783031791024;9783031791031

With the increasing incidence of neurodegenerative diseases such as Alzheimer's Disease (AD), there is a need for further research that enhances detection and monitoring of the diseases. We present MORPHADE (Morphological Autoencoders for Alzheimer's Disease Detection), a novel unsupervised learning approach which uses deformations to allow the analysis of 3D T1-weighted brain images. To the best of our knowledge, this is the first use of deformations with deep unsupervised learning to not only detect, but also localize and assess the severity of structural changes in the brain due to AD. We obtain markedly higher anomaly scores in clinically important areas of the brain in subjects with AD compared to healthy controls, showcasing that our method is able to effectively locate AD-related atrophy. We additionally observe a visual correlation between the severity of atrophy highlighted in our anomaly maps and medial temporal lobe atrophy scores evaluated by a clinical expert. Finally, our method achieves an AUROC of 0.80 in detecting AD, out-performing several supervised and unsupervised baselines. We believe our framework shows promise as a tool towards improved understanding, monitoring and detection of AD. To support further research and application, we have made our code publicly available at ***/ci-ber/MORPHADE.

关键词： Unsupervised learning Registration Classification

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Enhancing Software Defect Prediction in Ansible Scripts Using Code-Smell-Guided Prompting with Large Language Models in Edge-Cloud Infrastructures 4th

Enhancing Software Defect Prediction in Ansible Scripts Usin...

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24th International Conference Web Engineering (ICWE)

作者： Hong, Hyunsun Lee, Sungu Ryu, Duksan Baik, Jongmoon Korea Adv Inst Sci & Technol Daejeon South Korea Jeonbuk Natl Univ Jeonju South Korea

ISBN: (纸本)9783031751097;9783031751103

In edge-cloud systems, the quality of infrastructure deployment is crucial for delivering high-quality services, especially when using popular Infrastructure as Code (IaC) tools like Ansible. Ensuring the reliability of such large-scale code systems poses a significant challenge due to the limited testing resources. Software defect prediction (SDP) addresses this limitation by identifying defectprone software modules, allowing developers to prioritize testing resources effectively. This paper introduces a Large Language Model (LLM)-based approach for SDP in Ansible scripts with Code-Smell-guided Prompting (CSP). CSP leverages code smell indicators extracted from Ansible scripts to refine prompts given to LLMs, enhancing their understanding of code structure concerning defects. Our experimental results demonstrate that CSP variants, particularly the Chain of Thought CSP (CoT-CSP), outperform traditional prompting strategies, as evidenced by improved F1-scores and Recall. To the best of our knowledge, this is the first attempt to employ LLMs for SDP in Ansible scripts. By employing a code smell-guided prompting strategy tailored for Ansible, we anticipate that the proposed method will enhance software quality assurance and reliability, thereby increasing the overall reliability of edge-cloud systems.

关键词： Edge-cloud Ansible Large Language Models Software Defect Prediction

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A Novel Pipeline for Data Management and Analysis that Integrates Data Lakehouse Architecture into the Aeronautics Industry 5th

A Novel Pipeline for Data Management and Analysis that Integ...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Freitas, Nelson Vaqueira, Diogo Rocha, Andre Dionisio Barata, Jose Serrano, Fabio Silva, Luis Madeira, Manuel NOVA Univ Lisbon NOVA Sch Sci & Technol Ctr Technol & Syst UNINOVA CTS P-2829516 Lisbon Portugal NOVA Univ Lisbon NOVA Sch Sci & Technol Associated Lab Intelligent Syst LASI P-2829516 Lisbon Portugal Aernnova Aerosp Evora Parque Ind Aeronaut Evora Herdade Pinheiro E Casa Branca Lt AICM1094 P-7005797 Evora Portugal

ISBN: (纸本)9783031807596;9783031807602

Currently, the majority of processes or systems aim to benefit from the data produced by their own or other relevant systems, with the objective of increasing efficiency. This is especially true in the field of industrial systems, where a multitude of devices attempt to publish their metrics and data into the system, often resulting in characteristics that can be classified as big data. However, companies often struggle with the correct and useful utilization of this harvested data. Therefore, this paper focuses on a use case of a data pipeline system with a data lakehouse in an airplane parts factory. The developed architecture shows that with some adjustments to the classic data lakehouse architecture, it is possible to achieve higher parallelism in order to simultaneously store data in the data lake and data warehouse. Additionally, a visualization tool was developed to highlight how metric calculation and outlier detection can be automated or facilitated with the utilization of data, as opposed to manual labor.

关键词： Big data Data pipeline Data lake Data lakehouse Data warehouse Industry Manufacturing

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An Analysis on Balance Model of Exploration and Exploitation Under Decoupled-Learning Pattern for Large-Scale Particle Swarm Optimizers 5th

An Analysis on Balance Model of Exploration and Exploitation...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Ni, Wenke Guo, Weian Li, Dongyang Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Tongji Univ Sino German Coll Appl Sci Shanghai Peoples R China

ISBN: (纸本)9789819770007;9789819770014

To effectively tackle large-scale optimization problems, numerous efforts have been made by various swarm intelligence methodologies to strike a balance between two critical functions: exploration and exploitation. However, in the majority of existing particle swarm optimizers, these two functions often remain indistinguishable throughout the optimization process. To mitigate the mutual interference between exploration and exploitation, designers of optimizers have attempted to design these functions separately. Nonetheless, there has been limited research into analyzing the roles and the relationship between exploration and exploitation. Building upon our previous work, we have redesigned several state-of-the-art large-scale particle swarm optimizers using a decoupled learning pattern. Through comprehensive experimental analysis on the synergistic effect of exploitation and exploration, we delve into the optimization performance and introduce a mathematical model to illustrate their interrelationship. Our investigation and analysis reveal that most existing algorithms exhibit sensitivity in the parameter settings for exploration and exploitation, an aspect that has not been adequately addressed before. Based on the proposed mathematical analysis of the parameters, we provide clear balance model for algorithm designers and users on how to balance these two factors when solving large-scale optimization problems.

关键词： Large scale optimization problems Exploration and exploitation Decoupled learning pattern Balance model

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