A comprehensive review on machine learning in healthcare industry: classification, restrictions, opportunities and challenges
Recently, various sophisticated methods, including machine learning and artificial
intelligence, have been employed to examine health-related data. Medical professionals are …
intelligence, have been employed to examine health-related data. Medical professionals are …
Visuals to text: A comprehensive review on automatic image captioning
Y Ming, N Hu, C Fan, F Feng… - IEEE/CAA Journal of …, 2022 - researchportal.port.ac.uk
Image captioning refers to automatic generation of descriptive texts according to the visual
content of images. It is a technique integrating multiple disciplines including the computer …
content of images. It is a technique integrating multiple disciplines including the computer …
A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran
Floods are one of the most damaging natural hazards causing huge loss of property,
infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due …
infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due …
Machine learning techniques for chronic kidney disease risk prediction
Chronic kidney disease (CKD) is a condition characterized by progressive loss of kidney
function over time. It describes a clinical entity that causes kidney damage and affects the …
function over time. It describes a clinical entity that causes kidney damage and affects the …
[HTML][HTML] Flash flood susceptibility map** using a novel deep learning model based on deep belief network, back propagation and genetic algorithm
Flash floods are responsible for loss of life and considerable property damage in many
countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone …
countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone …
Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches
Nowadays, a number of machine learning prediction methods are being applied in the field
of landslide susceptibility modeling of the large area especially in the difficult hilly terrain. In …
of landslide susceptibility modeling of the large area especially in the difficult hilly terrain. In …
Quantifying hourly suspended sediment load using data mining models: case study of a glacierized Andean catchment in Chile
Suspended sediment has significant effects on reservoir storage capacity, the operation of
hydraulic structures and river morphology. Hence, modeling suspended sediment loads …
hydraulic structures and river morphology. Hence, modeling suspended sediment loads …
BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques
SK Pandey, RB Mishra, AK Tripathi - Expert Systems with Applications, 2020 - Elsevier
In software fault prediction systems, there are many hindrances for detecting faulty modules,
such as missing values or samples, data redundancy, irrelevance features, and correlation …
such as missing values or samples, data redundancy, irrelevance features, and correlation …
Multi-algorithm comparison for predicting soil salinity
Soil salinization is one of the most predominant processes responsible for land degradation
globally. However, monitoring large areas presents significant challenges due to strong …
globally. However, monitoring large areas presents significant challenges due to strong …
Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River …
S Saha, M Saha, K Mukherjee, A Arabameri… - Science of the Total …, 2020 - Elsevier
Rapid population growth and its corresponding effects like the expansion of human
settlement, increasing agricultural land, and industry lead to the loss of forest area in most …
settlement, increasing agricultural land, and industry lead to the loss of forest area in most …