Investigation of the Effectiveness of the Method for Recognizing Pre-Emergency Situations at Mining Facilities

Abu-Abed, Fares

doi:10.1051/e3sconf/202017402020

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Confirmatory research and ameliorated integration in the medical service flows become necessary for the maximization of the action and functional potential of the AI model [32,33]. Mathematical approaches for optimizing mining facilities have conducted us to the improvement of the AI apparatus [34,35]. Computational software associated with practiced fractal analysis, utilized in the present study, was first introduced and successfully detailed in the author's articles noted as the references [18][19][20].…”

Section: Discussionmentioning

confidence: 99%

The SARS-CoV-2 Virus Detection with the Help of Artificial Intelligence (AI) and Monitoring the Disease Using Fractal Analysis

Nichita,

Paun,

Paun

et al. 2023

Computers

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This paper introduces an AI model designed for the diagnosis and monitoring of the SARS-CoV-2 virus. The present artificial intelligence (AI) model founded on the machine learning concept was created for the identification/recognition, keeping under observation, and prediction of a patient’s clinical evaluation infected with the CoV-2 virus. The deep learning (DL)-initiated process (an AI subset) is punctually prepared to identify patterns and provide automated information to healthcare professionals. The AI algorithm is based on the fractal analysis of CT chest images, which is a practical guide to detecting the virus and establishing the degree of lung infection. CT pulmonary images, delivered by a free public source, were utilized for developing correct AI algorithms with the aim of COVID-19 virus observation/recognition, having access to coherent medical data, or not. The box-counting procedure was used with a predilection to determine the fractal parameters, the value of the fractal dimension, and the value of lacunarity. In the case of a confirmation, the analysed image is used as input data for a program responsible for measuring the degree of health impairment/damage using fractal analysis. The support of image scans with computer tomography assistance is solely the commencement part of a correctly established diagnostic. A profiled software framework has been used to perceive all the details collected. With the trained AI model, a maximum accuracy of 98.1% was obtained. This advanced procedure presents an important potential in the progress of an intricate medical solution to pulmonary disease evaluation.

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Section: Discussionmentioning

confidence: 99%

The SARS-CoV-2 Virus Detection with the Help of Artificial Intelligence (AI) and Monitoring the Disease Using Fractal Analysis

Nichita,

Paun,

Paun

et al. 2023

Computers

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“…Compared to traditional empirical formula methods, this approach demonstrates greater accuracy, objectivity, and efficiency, finding extensive applications in various sectors of the petroleum industry [19][20][21][22][23][24][25]. For example, Fares Abu-Abed [4][5][6] introduced a pattern recognition approach utilizing artificial neural networks for the identification and prediction of intricate scenarios within the drilling process. The approach employs accident statistical data extracted from the database as its input and enables the prediction of accidents, including blowouts, well leaks, and wellbore collapses.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning

Li,

Tan,

Deng

et al. 2023

Processes

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In recent years, there has been significant research and practical application of machine learning methods for predicting reservoir pore pressure. However, these studies frequently concentrate solely on reservoir blocks exhibiting normal-pressure conditions. Currently, there exists a scarcity of research addressing the prediction of pore pressure within reservoir blocks characterized by abnormally high pressures. In light of this, the present paper introduces a machine learning-based approach to predict pore pressure within reservoir blocks exhibiting abnormally high pressures. The methodology is demonstrated using the X block as a case study. Initially, the combination of the density–sonic velocity crossplot and the Bowers method is favored for elucidating the overpressure-to-compact mechanism within the X block. The elevated pressure within the lower reservoir is primarily attributed to the pressure generated during hydrocarbon formation. The Bowers method has been chosen to forecast the pore pressure in well X-1. Upon comparison with real pore pressure data, the prediction error is found to be under 5%, thus establishing it as a representative measure of the reservoir’s pore pressure. Intelligent prediction models for pore pressure were developed using the KNN, Extra Trees, Random Forest, and LightGBM algorithms. The models utilized five categories of well logging data, sonic time difference (DT), gamma ray (GR), density (ZDEN), neutron porosity (CNCF), and well diameter (CAL), as input. After training and comparison, the results demonstrate that the LightGBM model exhibits significantly superior performance compared to the other models. Specifically, it achieves R2 values of 0.935 and 0.647 on the training and test sets, respectively. The LightGBM model is employed to predict the pore pressure of two wells neighboring well X-1. Subsequently, the predicted data are juxtaposed with the actual pore pressure measurements to conduct error analysis. The achieved prediction accuracy exceeds 90%. This study delivers a comprehensive analysis of pore pressure prediction within sections exhibiting anomalously high pressure, consequently furnishing scientific insights to facilitate both secure and efficient drilling operations within the X block.

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The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning

Patlak,

Çunkaş,

Taskiran

2024

Arab J Sci Eng

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This paper presents an approach to prevent the incorrect transfer of fuel to the wrong tank during refueling. An experimental setup was developed to perform ultrasonic and dielectric measurements on diesel, gasoline, ethanol, and water. The fuel types were determined using an ultrasonic sensor and time-of-flight values were measured at various temperatures. Additionally, the dielectric coefficients of these liquids were measured to determine the liquid types using a dielectric sensor. The results obtained from both the ultrasonic and dielectric methods are systematically compared, and the advantages and disadvantages of each approach are thoroughly discussed. It was observed that dielectric method does not always yield accurate results. The proposed system effectively prevents erroneous transfer of fuel to the tank during refueling. The developed system may be used in practice to distinguish fuel types. In addition, a new approach using machine learning techniques to determine fuel type is presented. Fuel types were classified using 33 machine learning algorithms such as support vector machines, artificial neural networks and K-Nearest neighbors. It seems that artificial neural network with first layer size 25 and quadratic discriminant classifiers have achieved remarkable results with a success rate of 94% in classification. The results highlight the important and effective role of ultrasonic sensors in accurately identifying fuel types, leading to more efficient and safer storage and transportation of fuel. It is also concluded that machine learning techniques can be used effectively in identifying and classifying fuel types. The approach involving ultrasonic and artificial intelligence techniques was particularly innovative in distinguishing fuel types.

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Investigation of the Effectiveness of the Method for Recognizing Pre-Emergency Situations at Mining Facilities

Cited by 3 publications

References 11 publications

The SARS-CoV-2 Virus Detection with the Help of Artificial Intelligence (AI) and Monitoring the Disease Using Fractal Analysis

The SARS-CoV-2 Virus Detection with the Help of Artificial Intelligence (AI) and Monitoring the Disease Using Fractal Analysis

A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning

The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning

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