Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Engineering (7)
- Environmental Sciences (5)
- Mining Engineering (5)
- Oil, Gas, and Energy (5)
- Sustainability (5)
-
- Earth Sciences (4)
- Artificial Intelligence and Robotics (2)
- Computer Engineering (2)
- Computer Sciences (2)
- Business (1)
- Data Science (1)
- Digital Communications and Networking (1)
- Education (1)
- Explosives Engineering (1)
- Gifted Education (1)
- Higher Education (1)
- Liberal Studies (1)
- Numerical Analysis and Scientific Computing (1)
- Operations Research, Systems Engineering and Industrial Engineering (1)
- Programming Languages and Compilers (1)
- Real Estate (1)
- Systems Science (1)
- Theory and Algorithms (1)
- Institution
Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins
Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins
SMU Data Science Review
This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on …
Intelligent Identification Method Of Drilling Fluid Rheological Parameters Based On Machine Learning, Liu Changye, Yang Xianyu, Cai Jihua, Wang Ren, Wang Jianlong, Dai Fanfei, Guo Wanyang, Jiang Guoshe, Feng Yang
Intelligent Identification Method Of Drilling Fluid Rheological Parameters Based On Machine Learning, Liu Changye, Yang Xianyu, Cai Jihua, Wang Ren, Wang Jianlong, Dai Fanfei, Guo Wanyang, Jiang Guoshe, Feng Yang
Coal Geology & Exploration
The rheology of drilling fluid, which characterizes its flow and deformation, is vital for transporting and suspending rock cuttings as well as for enhancing the drilling rate. Precise control of drilling fluid rheological parameters is essential to ensure borehole cleanliness and efficient drilling. This paper proposes an intelligent identification method for drilling fluid rheological parameters based on Convolutional Neural Networks (CNNs). The method employs magnetic stirring to generate stable images of drilling fluid flow, uses various data augmentation methods to increase the number of images and create a database, thereby enhancing the model’s robustness and generalization capabilities. The AlexNet CNN …
Some Reflections On The Application Of Machine Learning To Research Into The Theoretical System Of Mine Water Prevention And Control, Yao Hui, Yin Huichao, Liang Manyu, Yin Shangxian, Hou Enke, Lian Huiqing, Xia Xiangxue, Zhang Jinfu, Wu Chuanshi
Some Reflections On The Application Of Machine Learning To Research Into The Theoretical System Of Mine Water Prevention And Control, Yao Hui, Yin Huichao, Liang Manyu, Yin Shangxian, Hou Enke, Lian Huiqing, Xia Xiangxue, Zhang Jinfu, Wu Chuanshi
Coal Geology & Exploration
The theoretical system of mine water prevention and control encompasses three fundamental aspects: disaster-causing mechanisms, risk evaluation, and disaster prediction. This theoretical system, having undergone rapid development over the past 20 years, aims to gain insights into the behavior characteristics of mine water and predict its evolutionary trend, thus serving the prevention and control of water disasters in mining areas. Applying machine learning, a powerful tool for data analysis and mining in the era of big data, to research into the theoretical system has garnered considerable attention. This study focuses on the specific applications of machine learning to the three …
An Intelligent Water Source Discrimination Method For Water Inrushes From Coal Seam Roofs In The Inner Mongolia-Shaanxi Border Region, Wang Hao, Sun Junqing, Zeng Yifan, Shang Hongbo, Wang Tiantian, Qiao Wei
An Intelligent Water Source Discrimination Method For Water Inrushes From Coal Seam Roofs In The Inner Mongolia-Shaanxi Border Region, Wang Hao, Sun Junqing, Zeng Yifan, Shang Hongbo, Wang Tiantian, Qiao Wei
Coal Geology & Exploration
Water hazard on the coal seam proof induced by high-intensity coal mining are increasingly prominent in the Inner Mongolia-Shaanxi border region. The effective, accurate water-source discrimination of the water inrushes is the key to water hazard prevention. This study investigated three typical mines in the Inner Mongolia-Shaanxi border region. To this end, principal component analysis (PCA) was employed to extract principal components from 80 groups of groundwater samples. Then, with inorganic indicators K++Na+, Ca2+, Mg2+, Cl−, SO4 2−, HCO3 − and TDS and organic indicators UV254 …
Early Warning And Prediction Of Kicks And Lost Circulation Accident During Rescue Drilling Of Mine, Chen Weiming, Wang Jiawen, Fan Dong, Hao Shijun, Zhao Jiangpeng, Qiu Yu
Early Warning And Prediction Of Kicks And Lost Circulation Accident During Rescue Drilling Of Mine, Chen Weiming, Wang Jiawen, Fan Dong, Hao Shijun, Zhao Jiangpeng, Qiu Yu
Coal Geology & Exploration
In order to solve the problems such as the difficulty in early warning and prediction of kicks and lost circulation accidents during emergency rescue drilling of mine, a machine learning-based early for warning and prediction model of drilling process was established. Firstly, the accident characterization parameters of the drilling parameters in the early stage of kicks and lost circulation accidents were analyzed. Secondly, the accident characterization parameters were cleaned and processed. On this basis, XGBoost and early warning model was used to carry out the early diagnosis and identification of kicks and lost circulation accidents. Then, the PSO-LSTM accident development …
Construction Of Machine Learning Data Set For Analyzing The Replay Of The Wargaming, Dayong Zhang, Jingyu Yang, Jun Ma, Chenye Song
Construction Of Machine Learning Data Set For Analyzing The Replay Of The Wargaming, Dayong Zhang, Jingyu Yang, Jun Ma, Chenye Song
Journal of System Simulation
Abstract: The first problem to be solved in the application of machine learning to the analysis of the replay of the wargaming is the construction of data sets. Due to the standardization requirements of machine learning for data structure, as well as the limitations of computing power and storage, building a machine learning data set through the wargaming data still faces many problems in terms of how to describe the wargaming situation, how to describe the wargaming process, how to handle high dimensional data, and how to prevent data distortion. To solve these problems, this paper constructs a mapping model …
Preprocessing Of Astronomical Images From The Neowise Survey For Near-Earth Asteroid Detection With Machine Learning, Rachel Meyer
Preprocessing Of Astronomical Images From The Neowise Survey For Near-Earth Asteroid Detection With Machine Learning, Rachel Meyer
ELAIA
Asteroid detection is a common field in astronomy for planetary defense, requiring observations from survey telescopes to detect and classify different objects. The amount of data collected each night is continually increasing as new and better-designed telescopes begin collecting information each year. This amount of data is quickly becoming unmanageable, and researchers are looking for ways to better process this data. The most feasible current solution is to implement computer algorithms to automatically detect these sources and then use machine learning to create a more efficient and accurate method of classification. Implementation of such methods has previously focused on larger …
Predicting Open-Pit Mine Production Using Machine Learning Techniques, Faustin Nartey Kumah, Alex Kwasi Saim, Millicent Nkrumah Oppong, Clement Kweku Arthur
Predicting Open-Pit Mine Production Using Machine Learning Techniques, Faustin Nartey Kumah, Alex Kwasi Saim, Millicent Nkrumah Oppong, Clement Kweku Arthur
Journal of Sustainable Mining
In mining, where production is affected by several factors, including equipment availability, it is necessary to develop reliable models to accurately predict mine production to improve operational efficiency. Hence, in this study, four (4) machine learning algorithms – namely: artificial neural network (ANN), random forest (RF), gradient boosting regression (GBR) and decision tree (DT)) – were implemented to predict mine production. Multiple Linear Regression (MLR) analysis was used as a baseline study for comparison purposes. In that regard, one hundred and twenty-six (126) datasets from an open-pit gold mine were used. The developed models were evaluated and compared using the …