scholarly journals Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment

2018 ◽  
Vol 42 (10) ◽  
Author(s):  
Abhishek Majumdar ◽  
Tapas Debnath ◽  
Sandeep K. Sood ◽  
Krishna Lal Baishnab
Keyword(s):  
Neural Network ◽  
Fog Computing ◽  
Disease Classification ◽  
Computing Environment ◽  
Forest Disease ◽  
Extremal Optimization ◽  
Classification Framework ◽  
Kyasanur Forest Disease

scholarly journals Improved Convolutional Neural Network for Chinese Sentiment Analysis in Fog Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Haoping Chen ◽  
Lukun Du ◽  
Yueming Lu ◽  
Hui Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Fog Computing ◽  
Predictive Performance ◽  
Specific Method ◽  
Data Filtering ◽  
Computing Environment ◽  
Automatic Feature Extraction ◽  
Analysis System

Fog computing extends the concept of cloud computing to the edge of network to relieve performance bottleneck and minimize data analytics latency at the central server of a cloud. It uses edge nodes directly to perform data input and data analysis. In public opinion analysis system, edge nodes that collect opinions from users are responsible for some data filtering jobs including sentiment analysis. Therefore, it is crucial to find suitable algorithm that is lightweight in operation and accurate in predictive performance. In this paper, we focus on Chinese sentiment analysis job in fog computing environment and propose a non-task-specific method called Channel Transformation Based Convolutional Neural Network (CTBCNN) for Chinese sentiment classification, which uses a new structure called channel transformation based (CTB) convolutional layer to enhance the ability of automatic feature extraction and applies global average pooling layer to prevent overfitting. Through experiments and analysis, we show that our method do achieve competitive accuracy and it is convenient to apply this method to different cases in operation.

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.

scholarly journals Low mammalian species richness is associated with Kyasanur forest disease outbreak risk in deforested landscapes in the Western Ghats, India

One Health ◽  
2021 ◽  
pp. 100299
Author(s):  
Michael G. Walsh ◽  
Rashmi Bhat ◽  
Venkatesh Nagarajan-Radha ◽  
Prakash Narayanan ◽  
Navya Vyas ◽  
...  
Keyword(s):  
Species Richness ◽  
Western Ghats ◽  
Mammalian Species ◽  
Disease Outbreak ◽  
Forest Disease ◽  
Kyasanur Forest Disease ◽  
The Western Ghats
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