Advanced Ground Water Level Prediction using KNN and Random Forest Algorithm
نویسندگان
چکیده
منابع مشابه
Diagnosis of Diabetes Using a Random Forest Algorithm
Background: Diabetes is the fourth leading cause of death in the world. And because so many people around the world have the disease, or are at risk for it, diabetes can be called the disease of the century. Diabetes has devastating effects on the health of people in the community and if diagnosed late, it can cause irreparable damage to vision, kidneys, heart, arteries and so on. Therefore, it...
متن کاملPrediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm
This work is devoted to the prediction of a series of 208 structurally diverse PKCθ inhibitors using the Random Forest (RF) based on the Mold(2) molecular descriptors. The RF model was established and identified as a robust predictor of the experimental pIC(50) values, producing good external R(2) (pred) of 0.72, a standard error of prediction (SEP) of 0.45, for an external prediction set of 51...
متن کاملA Random Forest Turbulence Prediction Algorithm
Unlike traditional pilot reports, in-situ EDR reports of atmospheric turbulence from commercial aircraft contain both positive and negative instances, are reported regularly, and have relatively accurate positions and timestamps. These data therefore make it feasible to perform more sophisticated analyses of the causes of atmospheric turbulence than were formerly possible. Several real-time gri...
متن کاملStock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm
Stock prices prediction is interesting and challenging research topic. Developed countries' economies are measured according to their power economy. Currently, stock markets are considered to be an illustrious trading field because in many cases it gives easy profits with low risk rate of return. Stock market with its huge and dynamic information sources is considered as a suitable environment ...
متن کاملSoftware Fault-proneness Prediction using Random Forest
Many metric-based classification models have been developed and applied to software fault-proneness prediction. This paper presents a novel prediction model using Random Forest classifier. Random Forest (RF) can be a promising candidate for software quality prediction because it is one of the most accurate classification algorithms available and has strengths in noise handling and efficient run...
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: International Journal for Research in Applied Science and Engineering Technology
سال: 2020
ISSN: 2321-9653
DOI: 10.22214/ijraset.2020.31515