000			04160nam a22001457a 4500
008			210805b ||||| |||| 00| 0 eng d
100			_aONIMSI, ThankGod Ayomide _98604
245			_aAVO and Seismic Attributes Analysis of OTG Field, Deep Offshore Niger Delta
250			_aProf. AYOLABI, Elijah and Mr. AKINWALE, R. P.
260			_aIbafo _bGeosciences _c2020
300			_ax,; 73p. _bdia, tables
520			_aClassification and identification of images from images from objects using machine learning. In our world today, object classification and identification helps detect and define objects such as humans, buildings and cars from digital images, it is a computer technology related computer vision and image processing. The aim was to identify and classify objects using convolutional neural network and the objectives; to formulate an algorithm, develop a model and evaluate the model. This project work presents the building process of a model to identify and classify object images from different objects. This chapter reports the design phase of the model, it involves decomposing the whole system into smaller parts and defining the relationship among the constituent parts. Top down design approach was employed in design and development phases of this project work. This involved dividing the system into subsystems or modules and each subsystem being further divided into even smaller subs. This process of division is repeated until each module is sufficiently small enough to be conveniently coded (implemented) from scratch as an independent entity that performs a clearly defined operation. At the beginning of the project, the tools to be used for this project were downloaded, the tools used for the project were the Kaggle Dataset API, Google Chrome, A Kaggle JSON file. One of the libraries used was matplotlib. Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. Classification and identification of images from images from objects using machine learning. In out world today,object classification and identification helps detect and define objects such as humans, buildings and cars from digital images, it is a computer technology related computer vision and image processing. The aim was to identify and classify objects using convolutional neural network and the objectives;to formulate an algorithm,develop a model and evaluate the model. This project work presents the building process of a model to identify and classify object images from different objects. This chapter reports the design phase of the model, it involves decomposing the whole system into smaller parts and defining the relationship among the constituent parts. vi Top down design approach was employed in design and development phases of this project work. This involved dividing the system into subsystems or modules and each subsystem being further divided into even smaller subs. This process of division is repeated until each module is sufficiently small enough to be conveniently coded (implemented) from scratch as an independent entity that performs a clearly defined operation. At the beginning of the project, the tools to be used for this project were downloaded, the tools used for the project were the Kaggle Dataset API, Google Chrome, A Kaggle JSON file. One of the libraries used was matplotlib. Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. All this methods and materials were deployed for this project and object classification and identification such as humans,cars,buildings was successful.
650			_aGeophysics _92162
942			_cTHS
999			_c6075 _d6075