Classification Framework for Clinical Datasets Using Synergistic Firefly Optimization

2022 ◽  
pp. 1-20
Author(s):  
V. R. Elgin Christo ◽  
H. Khanna Nehemiah ◽  
S. Keerthana Sankari ◽  
Shiney Jeyaraj ◽  
A. Kannan
Keyword(s):  
Classification Framework ◽  
Firefly Optimization

Array Design for the Synthesis of Stair Step Patterns Using Firefly Optimization Technique

2019 ◽  
Vol 9 (4) ◽  
pp. 285
Author(s):  
Krishna Rudraraju Chaitanya ◽  
P. Mallikarjuna Rao ◽  
K. V. S. N. Raju ◽  
G. S. N. Raju
Keyword(s):  
Optimization Technique ◽  
Array Design ◽  
Firefly Optimization

A SVD and Modified Firefly Optimization Based Robust Digital Image Watermarking Technique

2017 ◽  
Vol 7 (7) ◽  
pp. 268
Author(s):  
Chauhan Usha ◽  
Singh Rajeev Kumar
Keyword(s):  
Digital Media ◽  
Firefly Algorithm ◽  
Image Watermarking ◽  
Singular Values ◽  
Robust Watermarking ◽  
Scaling Factors ◽  
Geometric Attacks ◽  
Host Image ◽  
Firefly Optimization ◽  
Value Decomposition

Digital Watermarking is a technology, to facilitate the authentication, copyright protection and Security of digital media. The objective of developing a robust watermarking technique is to incorporate the maximum possible robustness without compromising with the transparency. Singular Value Decomposition (SVD) using Firefly Algorithm provides this objective of an optimal robust watermarking technique. Multiple scaling factors are used to embed the watermark image into the host by multiplying these scaling factors with the Singular Values (SV) of the host image. Firefly Algorithm is used to optimize the modified host image to achieve the highest possible robustness and transparency. This approach can significantly increase the quality of watermarked image and provide more robustness to the embedded watermark against various attacks such as noise, geometric attacks, filtering attacks etc.

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.

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