scholarly journals Glioma classification framework based on SE‐ResNeXt network and its optimization

2021 ◽  
Author(s):  
Jiang Linqi ◽  
Ning Chunyu ◽  
Li Jingyang
Keyword(s):  
Classification Framework ◽  
Glioma Classification

A Frequent Pattern Based Time Series Classification Framework

2010 ◽  
Vol 32 (2) ◽  
pp. 261-266
Author(s):  
Li Wan ◽  
Jian-xin Liao ◽  
Xiao-min Zhu ◽  
Ping Ni
Keyword(s):  
Time Series ◽  
Frequent Pattern ◽  
Time Series Classification ◽  
Classification Framework

Revisiting the Utsira Saline Aquifer CO2 Storage Resources Using the SRMS Classification Framework

2019 ◽  
Author(s):  
Sylvain Thibeau ◽  
Lesley Seldon ◽  
Franco Masserano ◽  
Jacobo Canal Vila ◽  
Philip Ringrose
Keyword(s):  
Co2 Storage ◽  
Saline Aquifer ◽  
Classification Framework

PUBLISHING PATTERNS IN SPECIALIST ACCOUNTING HISTORY JOURNALS IN THE ENGLISH LANGUAGE, 1996–1999

2000 ◽  
Vol 27 (2) ◽  
pp. 177-198 ◽  
Author(s):  
Garry D. Carnegie ◽  
Brad N. Potter
Keyword(s):  
Empirical Evidence ◽  
English Language ◽  
Time Span ◽  
International Accounting ◽  
Accounting History ◽  
Classification Framework ◽  
International Publishing ◽  
The Subject

While accounting researchers have explored international publishing patterns in the accounting literature generally, little is known about recent contributions to the specialist international accounting history journals. Specifically, this study surveys publishing patterns in the three specialist, internationally refereed, accounting history journals in the English language during the period 1996 to 1999. The survey covers 149 contributions in total and provides empirical evidence on the location of their authors, the subject country or region in each investigation, and the time span of each study. It also classifies the literature examined based on the literature classification framework provided by Carnegie and Napier [1996].

A Classification Framework for IoT Scenarios

2019 ◽  
pp. 458-469 ◽  
Author(s):  
Sankalita Mandal ◽  
Marcin Hewelt ◽  
Maarten Oestreich ◽  
Mathias Weske
Keyword(s):  
Classification Framework

scholarly journals Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1065
Author(s):  
Moshe Bensimon ◽  
Shlomo Greenberg ◽  
Moshe Haiut
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Analog Circuits ◽  
Continuous Time ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Sound Classification ◽  
Classification Framework ◽  
Neural Network Structure ◽  
Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

A Distinct Type of Online Group for Customer Knowledge Innovation: The Virtual Product User Community

Libri ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuguang Li ◽  
Andrew Cox
Keyword(s):  
Virtual Communities ◽  
Virtual Community ◽  
Distinct Type ◽  
Technical Problems ◽  
User Community ◽  
Customer Group ◽  
Knowledge Innovation ◽  
Classification Framework ◽  
User Communities ◽  
Virtual Product

Abstract Among online communities of customers there are a number of different types of group that need to be distinguished. One interesting type are virtual product user communities, i.e. company sponsored online forums where product users share usage experience and collaboratively construct new knowledge to solve technical problems. The purpose of this paper is to show that these “virtual product user communities” are a distinct type of customer group with knowledge innovation capability. The research adopts a method combining observation and content analysis of discussion threads where technical problems are solved, complemented by thematic analysis of interviews with forum members to explore its character, especially its knowledge related attributes. The paper confirms empirically that the virtual product user community is a distinct type of virtual community and can be differentiated from other virtual communities of consumers. In addition, an enhanced classification framework, extending Porter’s (2004) classic 5Ps model, is proposed to highlight knowledge-related activities in virtual communities. Of particular interest is that the findings suggest that knowledge-related activities should be considered as an important attribute in defining and classifying virtual communities. In terms of practical implications, it is recommended that the virtual product user community should be given appropriate support from top management in order to fully exploit its knowledge innovation value. Moreover, tailored facilitation strategies to promote knowledge construction activities and community development can be developed in accordance with its unique attributes. The paper precisely distinguishes one specific type of innovative virtual community consisting of product users from other online customer communities. Moreover, it outlines a revised virtual community classification framework, which can be widely applied in analysing features of online groups. Its key attribute of knowledge-related activity redirects attention to virtual communities’ knowledge innovation capabilities.

Improving accident analysis in construction – Development of a contributing factor classification framework and evaluation of its validity and reliability

2021 ◽  
Vol 140 ◽  
pp. 105303
Author(s):  
Anastacio Pinto Goncalves Filho ◽  
Patrick Waterson ◽  
Gyuchan Thomas Jun
Keyword(s):  
Accident Analysis ◽  
Validity And Reliability ◽  
Classification Framework

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Abhinav Kumar ◽  
Sanjay Kumar Singh ◽  
K Lakshmanan ◽  
Sonal Saxena ◽  
Sameer Shrivastava
Keyword(s):  
Differential Privacy ◽  
Model Performance ◽  
Healthcare Services ◽  
Cloud Services ◽  
Efficient Computation ◽  
Sensitive Data ◽  
Classification Framework ◽  
Proposed Model ◽  
Diagnostic Framework ◽  
Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

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