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Machine learning and discriminant function analysis in the formulation of generic models for sex prediction using patella measurements
(
Springer Nature
, 2022 , Article)
Sex prediction from bone measurements that display sexual dimorphism is one of the most important aspects of forensic anthropology. Some bones like the skull and pelvis display distinct morphological traits that are based ...
Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations
(
Elsevier
, 2020 , Article)
Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy ...
Shear capacity prediction of FRP-RC beams using single and ensenble ExPlainable Machine learning models
(
Elsevier
, 2022 , Article)
Corrosion in steel reinforcement is a central issue behind the severe deterioration of existing reinforced concrete (RC) structures. Nowadays, fiber-reinforced polymer (FRP) is increasingly being used as a viable alternative ...
Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
(
Elsevier
, 2022 , Article)
This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of ...
FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model
(
Elsevier
, 2022 , Article)
Fiber-reinforced polymer (FRP) composites have recently been considered in the field of structural engineering as one of the best alternatives to conventional steel reinforcement due to their high tensile strength, ...
Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites
(
Elsevier
, 2022 , Article)
The application of inorganic composites has proven to be an effective strengthening technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of the shear capacity of RC beams strengthened ...
A machine learning framework for enhancing digital experiences in cultural heritage
(
Emerald Group Publishing Ltd.
, 2020 , Article)
Purpose: Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which ...
Field data forecasting using lstm and bi-lstm approaches
(
MDPI
, 2021 , Article)
Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world's population growth. Proper irrigation scheduling has been shown to help farmers improve ...
Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques
(
MDPI AG
, 2020 , Article)
Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; ...
A nomogram-based diabetic sensorimotor polyneuropathy severity prediction using Michigan neuropathy screening instrumentations
(
Elsevier
, 2021 , Article)
Background: Diabetic Sensorimotor polyneuropathy (DSPN) is one of the major indelible complications in diabetic patients. Michigan neuropathy screening instrumentation (MNSI) is one of the most common screening techniques ...