Machine learning for wireless communications in the Internet of Things: A comprehensive survey
Abstract The Internet of Things (IoT) is expected to require more effective and efficient
wireless communications than ever before. For this reason, techniques such as spectrum …
wireless communications than ever before. For this reason, techniques such as spectrum …
Support vector machines: a recent method for classification in chemometrics
Y Xu, S Zomer, RG Brereton - Critical Reviews in Analytical …, 2006 - Taylor & Francis
Support Vector Machines (SVMs) are a new generation of classification method. Derived
from well principled Statistical Learning theory, this method attempts to produce boundaries …
from well principled Statistical Learning theory, this method attempts to produce boundaries …
GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods
X Chen, W Chen - Catena, 2021 - Elsevier
Globally, but especially in China, landslides are considered to be one of the most severe
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …
Machine-learning-assisted materials discovery using failed experiments
Inorganic–organic hybrid materials,, such as organically templated metal oxides, metal–
organic frameworks (MOFs) and organohalide perovskites have been studied for decades …
organic frameworks (MOFs) and organohalide perovskites have been studied for decades …
Crop yield prediction through proximal sensing and machine learning algorithms
Proximal sensing techniques can potentially survey soil and crop variables responsible for
variations in crop yield. The full potential of these precision agriculture technologies may be …
variations in crop yield. The full potential of these precision agriculture technologies may be …
Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue
Background In this study, the adsorption of methylene blue (MB) dye using an aquatic plant,
Azolla pinnata (AP) was modelled using several various supervised machine learning (ML) …
Azolla pinnata (AP) was modelled using several various supervised machine learning (ML) …
Congestive heart failure detection using random forest classifier
Background and objectives Automatic electrocardiogram (ECG) heartbeat classification is
substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of …
substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of …
Opcode sequences as representation of executables for data-mining-based unknown malware detection
Malware can be defined as any type of malicious code that has the potential to harm a
computer or network. The volume of malware is growing faster every year and poses a …
computer or network. The volume of malware is growing faster every year and poses a …
An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil
In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become
popular for constructing safe and sustainable pavement structures. The strength of the …
popular for constructing safe and sustainable pavement structures. The strength of the …
A machine learning approach to predict the average localization error with applications to wireless sensor networks
Node localisation is one of the significant concerns in Wireless Sensor Networks (WSNs). It
is a process in which we estimate the coordinates of the unknown nodes using sensors with …
is a process in which we estimate the coordinates of the unknown nodes using sensors with …