A comprehensive review on machine learning in healthcare industry: classification, restrictions, opportunities and challenges

Q An, S Rahman, J Zhou, JJ Kang - Sensors, 2023 - mdpi.com
Recently, various sophisticated methods, including machine learning and artificial
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 …

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

K Khosravi, BT Pham, K Chapi, A Shirzadi… - Science of the Total …, 2018 - Elsevier
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 …

Machine learning techniques for chronic kidney disease risk prediction

E Dritsas, M Trigka - Big Data and Cognitive Computing, 2022 - mdpi.com
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 …

[HTML][HTML] Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm

H Shahabi, A Shirzadi, S Ronoud, S Asadi, BT Pham… - Geoscience …, 2021 - Elsevier
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 …

Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

BT Pham, I Prakash, SK Singh, A Shirzadi, H Shahabi… - Catena, 2019 - Elsevier
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 …

Quantifying hourly suspended sediment load using data mining models: case study of a glacierized Andean catchment in Chile

K Khosravi, L Mao, O Kisi, ZM Yaseen, S Shahid - Journal of Hydrology, 2018 - Elsevier
Suspended sediment has significant effects on reservoir storage capacity, the operation of
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 …

Multi-algorithm comparison for predicting soil salinity

F Wang, Z Shi, A Biswas, S Yang, J Ding - Geoderma, 2020 - Elsevier
Soil salinization is one of the most predominant processes responsible for land degradation
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 …