Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

The breath rate can now be monitored remotely due to the advancements in digital stethoscope sensor technology, signal processing, and machine learning. Automatic breathing rate classification, on the other hand, provides additional benefits in medical diagnostics. In this paper, a lightweight convolutional neural network is proposed for automatic breathing rate classification utilizing the piezoresistive sensor data. In the proposed work, the raw signals from the piezoresistive sensor are pre-processed using a continuous wavelet transform to generate the corresponding images. These images are then fed into a lightweight convolutional neural network, which efficiently classifies the breathing rate into six classes based on the number of breaths per minute. Through extensive results, we show that the proposed model results in a classification accuracy of 96.40% which is higher than all the benchmark models considered in this paper. We also evaluate the performance of the proposed model using edge computing devices such as Raspberry Pi, Nvidia AGX Xavier, and Nvidia Jetson Nano.

Section: Non-contactmentioning

confidence: 99%

Breathing Rate Classification Using Piezoresistive Sensor Utilizing Continuous Wavelet Transform and Lightweight CNN

Gupta,

Yeduri,

Cenkeramaddi

2024

“…In recent years, the remarkable progress of deep learning has facilitated its extensive incorporation in diverse domains. Fields such as object detection [7], laser point cloud segmentation [8], and medical image processing [9] have all benefited from deep learning techniques. Notably, deep learning has made significant strides in the field of BAA, surpassing the performance of even seasoned experts.…”

Section: Introductionmentioning

confidence: 99%

KDALDL: Knowledge Distillation-Based Adaptive Label Distribution Learning Network for Bone Age Assessment

Zheng,

Yu,

et al. 2024

Deep learning-based bone age assessment (BAA) approaches have certain drawbacks, such as ignoring the correlation of age labels and simply assuming that bone development is linearly related to bone age, which can affect the accuracy of predictions. To solve these problems, a knowledge distillationbased adaptive label distribution learning method called KDALDL is proposed. The KDALDL framework comprises a teacher model and a student model, both consisting of modules for multi-scale feature extraction, feature refinement, and label distribution learning. First, a multi-scale feature extraction module is designed based on the swin transformer to extract feature information at various scales. Subsequently, these features are fed into the feature refinement module to capture the optimal image features. Then, the discrete labels obtained from the age labels through the Gaussian formula are used to train the teacher model. Finally, the teacher model's outputs are used to train the student model through the knowledge distillation technique, which enables the student model to achieve improved results by learning from the teacher model. The proposed method is validated using the Radiological Society of North America (RSNA) dataset, which exhibits outstanding results.INDEX TERMS Bone age assessment, Knowledge distillation, Label distribution learning, Swin transformer.

“…Deep neural networks (DNN) in informed paradigms exhibit excellent learning abilities with the advent and widespread use of deep learning techniques. Deep learning models take features from the original data and abstract them from the broad to the specific and perform incredibly well for feature extraction and data processing [23], [24].…”

Section: Introductionmentioning

confidence: 99%

A Novel Drug-Drug Indicator Dataset and Ensemble Stacking Model for Detection and Classification of Drug-Drug Interaction Indicators

Abbas,

Sampedro,

Abisado

et al. 2023

Drug-drug interaction (DDI) is a significant public health issue that accounts for 30% of unanticipated clinically hazardous medication events. The past decade has seen an evolution in informaticsbased research for DDI signal identification. This paper aims to create an ensemble stacking machine learning (ML) approach capable of accurately predicting novel DDI hazard indicators. The stacking ensemble machine learning architecture for predicting the signals of drug-drug interactions is supported by one of the most reliable sources of pharmacological data, DrugBank. We scrap a large dataset that contains drug-related information, including drug types, drug names, and other aspects of drug indicators, and make it publicly available to the research community. The proposed approach includes data preprocessing, balancing through a random oversampling, label encoding and one hot encoding technique used for categorical variables, and model prediction using an ensemble stacking technique. The proposed ensemble approach uses the Gradient Boosting (GB) classifier, Adaboost, and Gaussian Naive Bayes (GNB) to classify the drug indication types. The experimental results demonstrate that the suggested approach outperforms traditional machine learning approaches regarding accuracy and efficiency. The stacking model obtained the highest accuracy of 99.0%. The test indicates that the proposed model is more effective than traditional methods for detecting drug-drug interaction signals.