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.

Bibliografski detalji
Glavni autori:	Kabir, Md Saif, Alam, Shahed
Format:	Članak
Jezik:	English
Izdano:	BRAC University Research For Development Club (BURED) 2024
Teme:	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 pristup:	http://hdl.handle.net/10361/24426

Similar Items

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

Multimodal human detection in disaster situations using deep learning & artificial intelligence
od: Rafat, Md. Tasbi, i dr.
Izdano: (2023)

A deep learning approach for prediction of ADHD using brain structure
od: Siam, Shafin Ahad, i dr.
Izdano: (2024)

A secured federated learning system leveraging confidence score to identify retinal disease
od: Eshan, M Sakib Osman, i dr.
Izdano: (2023)

Alumni opportunities using machine learning
od: Siaum, Arshif Sharlimin, i dr.
Izdano: (2019)

Start up business solution using machine learning
od: Sohan, Saad Ahmed, i dr.
Izdano: (2018)

Machine learning approach for improving decision support in ICU
od: Siddiquee, Mohib Billah, i dr.
Izdano: (2019)

A machine learning approach to credit default prediction and Individual credit scoring
od: Rahman, Md. Jaber, i dr.
Izdano: (2018)

Yoga posture recognition using the deep learning process
od: Islam, Abidul, i dr.
Izdano: (2023)

Classifying insect pests from image data using deep learning
od: Mohsin, Md. Raiyan Bin, i dr.
Izdano: (2022)

Detecting propagandistic poster title: a machine learning approach
od: Mahmood, Riaz, i dr.
Izdano: (2024)

Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
od: Ferdous, Md.Rubaiat, i dr.
Izdano: (2024)

Machine learning approach for ECG analysis and predicting different heart diseases
od: Tithi, Sushmita Roy, i dr.
Izdano: (2019)

Predicting climate induced floods using machine learning
od: Saleh, Chowdhury Nafis, i dr.
Izdano: (2022)

Comparison of machine learning techniques to predict cardiovascular disease
od: Jaber, Mir Mohammad, i dr.
Izdano: (2019)

Application of machine learning in credit risk assessment: a prelude to smart banking
od: Dhruba, Mir Ishrak Maheer, i dr.
Izdano: (2019)

An implementation and analysis of deep learning models for the detection of wheat diseases
od: Zubair, Ahmad, i dr.
Izdano: (2024)

Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
od: Sakib, Hasibul, i dr.
Izdano: (2024)

Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
od: Sharma, Tanmoyee, i dr.
Izdano: (2021)

Implementation of diffusion model in realistic face generation
od: Rahman, Abidur, i dr.
Izdano: (2024)

Bank loan prediction using machine learning
od: Mahottam, Parthosarothi, i dr.
Izdano: (2023)

Real time performance analysis on DDoS attack detection using machine learning
od: Suvra, Debashis Kar, i dr.
Izdano: (2021)

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

Classification of retinal diseases from OCT images using deep learning models
od: Mokarrom, Md Saif, i dr.
Izdano: (2024)

Handwritten Bangla character recognition to braille pattern conversion using image processing and machine learning
od: Abir, Tawhidur Rahman, i dr.
Izdano: (2019)

An active-learning based training-schedule for biomedical image segmentation on deep neural networks
od: Hassan, Mehadi, i dr.
Izdano: (2021)

An advanced healthcare android application with machine learning
od: Ridwan, Naeeb MD, i dr.
Izdano: (2018)

Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection
od: Rahman, Masroor, i dr.
Izdano: (2023)

Generation of realistic images from human drawn sketches using deep learning
od: Mahbub, Mohammed Julfikar Ali, i dr.
Izdano: (2022)

Deep learning-based waste classification system for efficient waste management
od: Nakib, Abdullah Al, i dr.
Izdano: (2022)

Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
od: Ifty, Md. Hasin Sarwar, i dr.
Izdano: (2024)

Detection of fake identities on twitter using supervised machine learning
od: Rahman, MD. Takiur, i dr.
Izdano: (2019)

An efficient deep learning approach to classify white blood cells
od: Turja, Afif Ibna Kadir Khan, i dr.
Izdano: (2024)

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

Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning
od: Hussain, Nadib, i dr.
Izdano: (2018)

A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI
od: Mubin, Kazi Ehsanul, i dr.
Izdano: (2022)

Crop prediction based on geographical and climatic data using machine learning and deep learning
od: Alif, Al Amin, i dr.
Izdano: (2019)

Cancer classification using deep learning from medical image data
od: Monir, Raiyan Janik, i dr.
Izdano: (2022)

An interpretable transformer based approach to classify Malaria from blood cell images
od: Islam, Mehafuza, i dr.
Izdano: (2023)

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