DeepBAN: A Temporal Convolution-Based Communication Framework for Dynamic WBANs

Liu, Kunqian; Ke, Feng; Huang, Xin; Yu, Rong; Lin, Fan; Wu, Yueqian; Ng, Derrick Wing Kwan

doi:10.1109/tcomm.2021.3094581

Cited by 102 publications

(42 citation statements)

References 34 publications

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“…Data-driven CNN has achieved very good results in scientific research and engineering applications. In the field of natural images, Convolutional Neural Networks are widely used, such as image classification and object recognition, which have achieved good results and have received great attention from academia and industrial streets [ 30 – 33 ]. Therefore, some researchers have introduced CNN into the study of medical images, such as image segmentation in brain magnetic resonance images, detection of lung nodules in lung CT images, and detection and classification in breast tissue pathological images [ 34 ].…”

Section: Deep Transfer Learning Algorithms and Applicationsmentioning

confidence: 99%

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction

Pan

et al. 2022

Journal of Healthcare Engineering

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Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.

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Section: Deep Transfer Learning Algorithms and Applicationsmentioning

confidence: 99%

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction

Pan

et al. 2022

Journal of Healthcare Engineering

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“…The literature describes the time-frequency cepstrum coefficient. First, we study the Fourier transform and the short-time Fourier transform, compare and analyze the two, and then study the knowledge related to the Mel frequency cepstrum coefficient based on the short-time Fourier transform and analyze the extraction process of the Mel frequency cepstrum coefficient to study the wavelet definition and characteristics of transform, compare and analyze wavelet transform and Fourier transform, and finally study the Coch filter cepstrum system based on wavelet transform [11][12][13]. The literature introduces convolutional neural networks based on auditory features.…”

Section: Related Workmentioning

confidence: 99%

Distributed Sound Transmission and Smart City Planning Management Based on Convolutional Neural Network

2022

Wireless Communications and Mobile Computing

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In this article, we provide a detection method based on a convolutional neural network algorithm. Preamble detection is very important in underwater acoustic communication. Only the preamble signal can trigger the receiving operation to effectively process the acoustic data received underwater. The prerequisite for specifying the joint position of the distributed acoustic multiarray is the mutual calibration calculation of each array. This paper designs and develops a prototype system for microphone array sound source positioning and provides an optimized system to obtain calibration source mapping points for distributed sound transmission mutual calibration, and for mutual distributed acoustic microphone arrays, use epipolar geometry and normalization algorithm for calibration to provide a basis for a mutual calibration calculation model and functional module design. Finally, perform numerical simulation verification. It provides a design plan and implementation method for the Internet-based smart city education system, aiming to achieve professional education and educational needs related to the Internet of Things. First, we will take Internet application technology as an example to analyze the functional requirements of the education system, including the positioning of human resource development, integrated sensors, C/C#/Java application development technology, and other technical means to realize the smart city education system. Based on the theory of business management, this paper studies the business planning issues of smart city construction. The main content is divided into three parts: business composition and management and planning management. This is an important part of business. It plays a decisive role in the overall planning and implementation stage and protects the implementation progress of the project and ensures the quality of the project by including important content.

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“…One of the methods used to teach deep networking is the deep belief network. The deep belief network [87,88] has become a popular approach in machine learning due to its advantages such as fast inference and the ability to encode richer and higher-order network structures. DBN operates a hierarchical structure with several finite Boltzmann machines, and operates through a layered learning process [89].…”

Section: Deep Belief Networkmentioning

confidence: 99%

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

Ghasemian

Shahabi

Shirzadi

et al. 2022

Sensors

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We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.

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DeepBAN: A Temporal Convolution-Based Communication Framework for Dynamic WBANs

Cited by 102 publications

References 34 publications

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction

Distributed Sound Transmission and Smart City Planning Management Based on Convolutional Neural Network

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

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