Impact of machine learning and deep learning on biomedical applications and healthcare industry

Impact of machine learning and deep learning on biomedical applications and healthcare industry

Cataloged from PDF version of the article.

Bibliografiske detaljer
Main Authors:	Kabir, Md Saif, Alam, Shahed
Format:	Article
Sprog:	English
Udgivet:	BRAC University Research For Development Club (BURED) 2024
Fag:	Biomedical engineering Machine learning Deep learning Healthcare Data-driven insights Medical diagnostics Artificial intelligence Artificial intelligence. Data mining. Healthcare. Medical care. Biomedical engineering. Machine learning.
Online adgang:	http://hdl.handle.net/10361/24426

Lignende værker

A review on the role of artificial intelligence in healthcare transformation
af: Brishti, Iffat Ara
Udgivet: (2024)

Multimodal human detection in disaster situations using deep learning & artificial intelligence
af: Rafat, Md. Tasbi, et al.
Udgivet: (2023)

A deep learning approach for prediction of ADHD using brain structure
af: Siam, Shafin Ahad, et al.
Udgivet: (2024)

A secured federated learning system leveraging confidence score to identify retinal disease
af: Eshan, M Sakib Osman, et al.
Udgivet: (2023)

Alumni opportunities using machine learning
af: Siaum, Arshif Sharlimin, et al.
Udgivet: (2019)

Start up business solution using machine learning
af: Sohan, Saad Ahmed, et al.
Udgivet: (2018)

Machine learning approach for improving decision support in ICU
af: Siddiquee, Mohib Billah, et al.
Udgivet: (2019)

A machine learning approach to credit default prediction and Individual credit scoring
af: Rahman, Md. Jaber, et al.
Udgivet: (2018)

Yoga posture recognition using the deep learning process
af: Islam, Abidul, et al.
Udgivet: (2023)

Classifying insect pests from image data using deep learning
af: Mohsin, Md. Raiyan Bin, et al.
Udgivet: (2022)

Detecting propagandistic poster title: a machine learning approach
af: Mahmood, Riaz, et al.
Udgivet: (2024)

Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
af: Ferdous, Md.Rubaiat, et al.
Udgivet: (2024)

Machine learning approach for ECG analysis and predicting different heart diseases
af: Tithi, Sushmita Roy, et al.
Udgivet: (2019)

Predicting climate induced floods using machine learning
af: Saleh, Chowdhury Nafis, et al.
Udgivet: (2022)

Comparison of machine learning techniques to predict cardiovascular disease
af: Jaber, Mir Mohammad, et al.
Udgivet: (2019)

Application of machine learning in credit risk assessment: a prelude to smart banking
af: Dhruba, Mir Ishrak Maheer, et al.
Udgivet: (2019)

An implementation and analysis of deep learning models for the detection of wheat diseases
af: Zubair, Ahmad, et al.
Udgivet: (2024)

Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
af: Sakib, Hasibul, et al.
Udgivet: (2024)

Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
af: Sharma, Tanmoyee, et al.
Udgivet: (2021)

Implementation of diffusion model in realistic face generation
af: Rahman, Abidur, et al.
Udgivet: (2024)

Bank loan prediction using machine learning
af: Mahottam, Parthosarothi, et al.
Udgivet: (2023)

Real time performance analysis on DDoS attack detection using machine learning
af: Suvra, Debashis Kar, et al.
Udgivet: (2021)

The integration of AI and human contribution by Augmedix in assisting the US healthcare system
af: Zaheen, Tasfia
Udgivet: (2024)

Classification of retinal diseases from OCT images using deep learning models
af: Mokarrom, Md Saif, et al.
Udgivet: (2024)

Handwritten Bangla character recognition to braille pattern conversion using image processing and machine learning
af: Abir, Tawhidur Rahman, et al.
Udgivet: (2019)

An active-learning based training-schedule for biomedical image segmentation on deep neural networks
af: Hassan, Mehadi, et al.
Udgivet: (2021)

An advanced healthcare android application with machine learning
af: Ridwan, Naeeb MD, et al.
Udgivet: (2018)

Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection
af: Rahman, Masroor, et al.
Udgivet: (2023)

Generation of realistic images from human drawn sketches using deep learning
af: Mahbub, Mohammed Julfikar Ali, et al.
Udgivet: (2022)

Deep learning-based waste classification system for efficient waste management
af: Nakib, Abdullah Al, et al.
Udgivet: (2022)

Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
af: Ifty, Md. Hasin Sarwar, et al.
Udgivet: (2024)

Detection of fake identities on twitter using supervised machine learning
af: Rahman, MD. Takiur, et al.
Udgivet: (2019)

An efficient deep learning approach to classify white blood cells
af: Turja, Afif Ibna Kadir Khan, et al.
Udgivet: (2024)

Quantum lattice learning and explainable artificial intelligence for maternal and child healthcare
af: Marvin, Ggaliwango
Udgivet: (2022)

Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning
af: Hussain, Nadib, et al.
Udgivet: (2018)

A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI
af: Mubin, Kazi Ehsanul, et al.
Udgivet: (2022)

Crop prediction based on geographical and climatic data using machine learning and deep learning
af: Alif, Al Amin, et al.
Udgivet: (2019)

Cancer classification using deep learning from medical image data
af: Monir, Raiyan Janik, et al.
Udgivet: (2022)

An interpretable transformer based approach to classify Malaria from blood cell images
af: Islam, Mehafuza, et al.
Udgivet: (2023)

Comparative analysis of machine learning models for the prediction of asthma disease among the cardiovascular disease patients
af: Jahan, Asif
Udgivet: (2024)