Fog computing: A taxonomy, systematic review, current trends and research challenges
There has been rapid development in the number of Internet of Things (IoT) connected
nodes and devices in our daily life in recent times. With this increase in the number of …
nodes and devices in our daily life in recent times. With this increase in the number of …
[HTML][HTML] Bibliometric literature review of adaptive learning systems
In this review paper, we computationally analyze a vast volume of published articles in the
field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a …
field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a …
[HTML][HTML] Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms
The healthy liver plays more than 500 organic roles in the human body, while a malfunction
may be dangerous or even deadly. Early diagnosis and treatment of liver disease can …
may be dangerous or even deadly. Early diagnosis and treatment of liver disease can …
An efficient machine learning-based resource allocation scheme for SDN-enabled fog computing environment
Fog computing is an emerging technology which enables computing resources accessibility
close to the end-users. It overcomes the drawbacks of available network bandwidth and …
close to the end-users. It overcomes the drawbacks of available network bandwidth and …
[HTML][HTML] Energy-efficient and secure load balancing technique for SDN-enabled fog computing
The number of client applications on the fog computing layer is increasing due to
advancements in the Internet of Things (IoT) paradigm. Fog computing plays a significant …
advancements in the Internet of Things (IoT) paradigm. Fog computing plays a significant …
[HTML][HTML] Statistical machine learning approaches to liver disease prediction
Medical diagnoses have important implications for improving patient care, research, and
policy. For a medical diagnosis, health professionals use different kinds of pathological …
policy. For a medical diagnosis, health professionals use different kinds of pathological …
CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect liver diseases on cloud
Liver disease is a significant global burden on health, with about a few hundred million
people suffering from chronic liver disease (CLD), with approximately 2 million deaths each …
people suffering from chronic liver disease (CLD), with approximately 2 million deaths each …
Supervised machine learning based liver disease prediction approach with LASSO feature selection
S Afrin, FMJM Shamrat, TI Nibir, MF Muntasim… - Bulletin of Electrical …, 2021 - beei.org
In this contemporary era, the uses of machine learning techniques are increasing rapidly in
the field of medical science for detecting various diseases such as liver disease (LD) …
the field of medical science for detecting various diseases such as liver disease (LD) …
An effective approach for early liver disease prediction and sensitivity analysis
The liver is one of the most vital organs of the human body. Even when partially injured, it
functions normally. Therefore, detecting liver diseases at the early stages is challenging …
functions normally. Therefore, detecting liver diseases at the early stages is challenging …
Feature selection and classification of clinical datasets using bioinspired algorithms and super learner
S Murugesan, RS Bhuvaneswaran… - … Methods in Medicine, 2021 - Wiley Online Library
A computer‐aided diagnosis (CAD) system that employs a super learner to diagnose the
presence or absence of a disease has been developed. Each clinical dataset is …
presence or absence of a disease has been developed. Each clinical dataset is …