[HTML][HTML] An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)
M Desai, M Shah - Clinical eHealth, 2021 - Elsevier
This paper aims to review Artificial neural networks, Multi-Layer Perceptron Neural network
(MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for …
(MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for …
[HTML][HTML] Ai in thyroid cancer diagnosis: Techniques, trends, and future directions
Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years,
offering advanced tools and methodologies that promise to revolutionize patient outcomes …
offering advanced tools and methodologies that promise to revolutionize patient outcomes …
Underwater targets classification using local wavelet acoustic pattern and Multi-Layer Perceptron neural network optimized by modified Whale Optimization Algorithm
W Qiao, M Khishe, S Ravakhah - Ocean Engineering, 2021 - Elsevier
Considering heterogeneities and difficulties in the classification of underwater passive
targets, this paper proposes the use of Local Wavelet Acoustic Pattern (LWAP) and Multi …
targets, this paper proposes the use of Local Wavelet Acoustic Pattern (LWAP) and Multi …
[HTML][HTML] A hybrid computer-aided-diagnosis system for prediction of breast cancer recurrence (HPBCR) using optimized ensemble learning
Cancer is a collection of diseases that involves growing abnormal cells with the potential to
invade or spread to the body. Breast cancer is the second leading cause of cancer death …
invade or spread to the body. Breast cancer is the second leading cause of cancer death …
A review of epidemic forecasting using artificial neural networks
PM Datilo, Z Ismail, J Dare - Epidemiology and Health System …, 2019 - ehsj.skums.ac.ir
Background and aims: Since accurate forecasts help inform decisions for preventive health-
care intervention and epidemic control, this goal can only be achieved by making use of …
care intervention and epidemic control, this goal can only be achieved by making use of …
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model
F Haghighat - Chaos, Solitons & Fractals, 2021 - Elsevier
Although more than a year has passed since the coronavirus outbreak globally, the Covid-
19 pandemic conditions still exist in many countries, including Iran. Predicting the number of …
19 pandemic conditions still exist in many countries, including Iran. Predicting the number of …
LSTM with working memory
A Pulver, S Lyu - … Joint Conference on Neural Networks (IJCNN …, 2017 - ieeexplore.ieee.org
Previous RNN architectures have largely been superseded by LSTM, or “Long Short-Term
Memory”. Since its introduction, there have been many variations on this simple design …
Memory”. Since its introduction, there have been many variations on this simple design …
Comparison of machine learning models to provide preliminary forecasts of real estate prices
Real estate is one of the most critical investments in the household portfolio, and represents
the greatest proportion of wealth of the private households in highly developed countries …
the greatest proportion of wealth of the private households in highly developed countries …
On training efficiency and computational costs of a feed forward neural network: A review
A comprehensive review on the problem of choosing a suitable activation function for the
hidden layer of a feed forward neural network has been widely investigated. Since the …
hidden layer of a feed forward neural network has been widely investigated. Since the …
Convexified convolutional neural networks
We describe the class of convexified convolutional neural networks (CCNNs), which capture
the parameter sharing of convolutional neural networks in a convex manner. By …
the parameter sharing of convolutional neural networks in a convex manner. By …