Classification of soil quality using K-Nearest Neighbors methods
نویسندگان
چکیده
منابع مشابه
Predicting Medical Conditions Using k-Nearest Neighbors
As the healthcare industry becomes more reliant upon electronic records, the amount of medical data available for analysis increases exponentially. While this information contains valuable statistics, the sheer volume makes it difficult to analyze without efficient algorithms. By using machine learning to classify medical data, diagnoses can become more efficient, accurate, and accessible for t...
متن کاملOptical Character Recognition, Using K-Nearest Neighbors
The problem of optical character recognition, OCR, has been widely discussed in the literature. Having a hand-written text, the program aims at recognizing the text. Even though there are several approaches to this issue, it is still an open problem. In this paper we would like to propose an approach that uses K-nearest neighbors algorithm, and has the accuracy of more than 90%. The training an...
متن کاملAdaptive k-Nearest-Neighbor Classification Using a Dynamic Number of Nearest Neighbors
Classification based on k-nearest neighbors (kNN classification) is one of the most widely used classification methods. The number k of nearest neighbors used for achieving a high accuracy in classification is given in advance and is highly dependent on the data set used. If the size of data set is large, the sequential or binary search of NNs is inapplicable due to the increased computational ...
متن کاملArticulatory Feature Classification Using Nearest Neighbors
Recognizing aspects of articulation from audio recordings of speech is an important problem, either as an end in itself or as part of an articulatory approach to automatic speech recognition. In this paper we study the frame-level classification of a set of articulatory features (AFs) inspired by the vocal tract variables of articulatory phonology. We compare k nearest neighbor (k-NN) classifie...
متن کاملA comparative study of performance of K-nearest neighbors and support vector machines for classification of groundwater
The aim of this work is to examine the feasibilities of the support vector machines (SVMs) and K-nearest neighbor (K-NN) classifier methods for the classification of an aquifer in the Khuzestan Province, Iran. For this purpose, 17 groundwater quality variables including EC, TDS, turbidity, pH, total hardness, Ca, Mg, total alkalinity, sulfate, nitrate, nitrite, fluoride, phosphate, Fe, Mn, Cu, ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IOP Conference Series: Earth and Environmental Science
سال: 2021
ISSN: 1755-1307,1755-1315
DOI: 10.1088/1755-1315/739/1/012011