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What is relevant in a text document a machine learning based approach
(Brac University, 2021-06)
Text Documents often contain valuable data. But not all data is relevant. That is
why extracting relevant data from text documents is an essential task. Extracting
relevant data from text documents refers to the study ...
Predicting regional accents of Bengali language using deep learning
(Brac University, 2021-09)
Accent is a huge challenge in communication for all languages. Different people
who speak the same language might pronounce the same word differently. In a
conversation, if two people are from different regions and they ...
Cancer classification using deep learning from medical image data
(Brac University, 2022-01)
Cancer is a disease in which some of the body’s cells grow uncontrollably and spread
to other parts of the body. Cancer can start almost anywhere in the human body,
which is made up of trillions of cells. There is usually ...
Eve-teasing detection from video footage using computer vision and artificial intelligence
(Brac University, 2023-01)
We present computer vision approaches combined with machine learning techniques
to detect eve-teasing from any video material, which may be used in any situa tion. Eve teasing is a colloquial term for public sexual ...
Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning
(Brac University, 2020-04)
Parkinson’s disease is a neurological condition that is dynamic and steadily influences the movement of the human body. It causes issues within the brain and slowly
increments time by time. Tremor is the major side effect ...
Detecting brain tumor using deep neural networks from MRI images
(Brac University, 2021-06)
A brain tumor is a collection of abnormal cells growth in brain. It is a neurological
disease which causes great damage and affects other healthy cells of brain. It can be
cancerous or non-cancerous. Nowadays, people are ...
An efficient approach for binary classification in brain tumor detection using convolutional neural network
(Brac University, 2022-01)
Brain tumor detection using Convolutional Neural Network (CNN) models with
binary classification has significantly improved the reliability of medical imaging
through Deep Learning. The purpose of this research is to ...
An efficient deep learning approach to detect COVID-19 infected lungs using image data
(Brac University, 2022-01)
The beginning of 2020 will always be a dreadful chapter in human history. Even
with all the recent advancements in the medical sector, the COVID-19 virus proved
to be a major challenge for doctors all over the world. The ...
An enhanced CNN model for classifying skin cancer
(Brac University, 2022-01)
Unrepaired deoxyribonucleic acid in skin cells causes skin cancer by generating genetic
abnormalities or mutations, rising day by day. Detecting and diagnosing skin
cancer in its early stages is expensive and challenging, ...
Leveraging robust CNN architectures for real-time object recognition from conveyor belt
(Brac University, 2023-01)
In the innovative era, the problem of recognizing undesirable objects and individuals
on conveyor belts is addressed by various architectural or algorithmic approaches.
Conveyor belts are those by which things go in a ...