Diabetes, resulting from inadequate insulin production or utilization, causes
extensive harm to the body. Existing diagnostic methods are often invasive and
come with drawbacks, such as cost constraints. Although there are machine
learning models like Classwise k Nearest Neighbor (CkNN) and General Regression
Neural Network (GRNN), they struggle with imbalanced data and result in
under-performance. Leveraging advancements in sensor technology and machine
learning, we propose a non-invasive diabetes diagnosis using a Back Propagation
Neural Network (BPNN) with batch normalization, incorporating data re-sampling
and normalization for class balancing. Our method addresses existing challenges
such as limited performance associated with traditional machine learning.
Experimental results on three datasets show significant improvements in overall
accuracy, sensitivity, and specificity compared to traditional methods.
Notably, we achieve accuracies of 89.81% in Pima diabetes dataset, 75.49% in
CDC BRFSS2015 dataset, and 95.28% in Mesra Diabetes dataset. This underscores
the potential of deep learning models for robust diabetes diagnosis. See
project website this https URL

利用传感器技术和机器学习，我们提出了一种基于批归一化的反向传播神经网络（BPNN）的非侵入性糖尿病诊断方法，通过数据重采样和标准化实现类别平衡，实验结果显示相对传统方法在整体准确度、敏感度和特异度上具有显著提高，特别是在 Pima 糖尿病数据集中达到了 89.81% 的准确度，在 CDC BRFSS2015 数据集中达到了 75.49% 的准确度，在 Mesra 糖尿病数据集中达到了 95.28% 的准确度，凸显了深度学习模型在稳健糖尿病诊断方面的潜力。

深度学习在糖尿病诊断中的应用

A Deep Learning Approach to Diabetes Diagnosis

In this paper, we seek a new method in designing an iris recognition system.
In this method, first the Haar wavelet features are extracted from iris images.
The advantage of using these features is the high-speed extraction, as well as
being unique to each iris. Then the back propagation neural network (BPNN) is
used as a classifier. In this system, the BPNN parallel algorithms and their
implementation on GPUs have been used by the aid of CUDA in order to speed up
the learning process. Finally, the system performance and the speeding outcomes
in a way that this algorithm is done in series are presented.

通过使用 Haar 小波特征和 BPNN 并行算法及其在 GPU 上的实现，本研究提出了一种新的虹膜识别系统，以实现高速的识别和学习过程。

使用 BP 神经网络和 GPU 并行计算辅助的一种新型虹膜识别方法

A novel method for iris recognition using BP neural network and parallel  computing by the aid of GPUs (Graphics Processing Units)

Astronomical images provide information about the great variety of cosmic
objects in the Universe. Due to the large volumes of data, the presence of
innumerable bright point sources as well as noise within the frame and the
spatial gap between objects and satellite cameras, it is a challenging task to
classify and detect the celestial objects. We propose an Adaptive Thresholding
Method (ATM) based segmentation and Back Propagation Neural Network (BPNN)
based cosmic object detection including a well-structured series of
pre-processing steps designed to enhance segmentation and detection.

基于自适应阈值分割方法和反向传播神经网络的天体物体检测，采用了一系列预处理步骤以增强分割和检测。