본 논문에서는 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 노이즈에 강한 특성을 보이는 퍼지 집합 이론을 이용한 새로운 패턴 분류기를 제안 한다. 기존 인공 신경망에 비해 학습속도가 매우 빠르며, 모델의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 학습 알고리즘을 퍼지 패턴 분류기에 적용하여 퍼지 패턴 분류기의 학습 속도와 패턴 분류 일반화 성능을 개선 한다. 제안된 퍼지 패턴 분류기의 학습 속도와 일반화 성능을 평가하기 위하여, 다양한 머신 러닝 데이터 집합을 사용한다.

Extreme Learning Machine 기반 퍼지 패턴 분류기 설계

The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.

A survey on nature inspired metaheuristic algorithms for partitional clustering

Review of applications of algorithms in bio-inspired computing.Brief description of algorithms without mathematical notations.Brief description of scope of applications of the algorithms.Identification of algorithms whose applications may be explored.Identification of algorithms on which theory development may be explored. With the explosion of data generation, getting optimal solutions to data driven problems is increasingly becoming a challenge, if not impossible. It is increasingly being recognised that applications of intelligent bio-inspired algorithms are necessary for addressing highly complex problems to provide working solutions in time, especially with dynamic problem definitions, fluctuations in constraints, incomplete or imperfect information and limited computation capacity. More and more such intelligent algorithms are thus being explored for solving different complex problems. While some studies are exploring the application of these algorithms in a novel context, other studies are incrementally improving the algorithm itself. However, the fast growth in the domain makes researchers unaware of the progresses across different approaches and hence awareness across algorithms is increasingly reducing, due to which the literature on bio-inspired computing is skewed towards few algorithms only (like neural networks, genetic algorithms, particle swarm and ant colony optimization). To address this concern, we identify the popularly used algorithms within the domain of bio-inspired algorithms and discuss their principles, developments and scope of application. Specifically, we have discussed the neural networks, genetic algorithm, particle swarm, ant colony optimization, artificial bee colony, bacterial foraging, cuckoo search, firefly, leaping frog, bat algorithm, flower pollination and artificial plant optimization algorithm. Further objectives which could be addressed by these twelve algorithms have also be identified and discussed. This review would pave the path for future studies to choose algorithms based on fitment. We have also identified other bio-inspired algorithms, where there are a lot of scope in theory development and applications, due to the absence of significant literature.

Bio inspired computing - A review of algorithms and scope of applications

Classification Methods For Remotely Sensed Data

Swarm intelligence (SI), an integral part in the field of artificial intelligence, is gradually gaining prominence, as more and more high complexity problems require solutions which may be sub-optimal but yet achievable within a reasonable period of time. Mostly inspired by biological systems, swarm intelligence adopts the collective behaviour of an organized group of animals, as they strive to survive. This study aims to discuss the governing idea, identify the potential application areas and present a detailed survey of eight SI algorithms. The newly developed algorithms discussed in the study are the insect-based algorithms and animal-based algorithms in minute detail. More specifically, we focus on the algorithms inspired by ants, bees, fireflies, glow-worms, bats, monkeys, lions and wolves. The inspiration analyses on these algorithms highlight the way these algorithms operate. Variants of these algorithms have been introduced after the inspiration analysis. Specific areas for the application of such algorithms have also been highlighted for researchers interested in the domain. The study attempts to provide an initial understanding for the exploration of the technical aspects of the algorithms and their future scope by the academia and practice.

Swarm Intelligence: A Review of Algorithms

This paper presents an optimized watermarking scheme based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The singular values of a binary watermark are embedded in singular values of the LL3 sub-band coefficients of the host image by making use of multiple scaling factors (MSFs). The MSFs are optimized using a newly proposed Firefly Algorithm having an objective function which is a linear combination of imperceptibility and robustness. The PSNR values indicate that the visual quality of the signed and attacked images is good. The embedding algorithm is robust against common image processing operations. It is concluded that the embedding and extraction of the proposed algorithm is well optimized, robust and show an improvement over other similar reported methods.

Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm

Gray-scale image watermarking using GA-BPN hybrid network

A novel gray-scale image watermarking using hybrid Fuzzy-BPN architecture

A novel, nature inspired, unsupervised classification method, based on the most recent metaheuristic algorithm, stirred by the breeding strategy of the parasitic bird, the cuckoo, is introduced in this paper. The proposed Cuckoo Search Clustering Algorithm (CSCA) yields good results on benchmark dataset. Inspired by the results, the proposed algorithm is validated on two real time remote sensing satellite- image datasets for extraction of the water body, which itself is a quite complex problem. The CSCA makes use of Davies-Bouldin index (DBI) as fitness function. Also a method for generation of new cuckoos used in this algorithm is introduced. The resulting algorithm is conceptually simpler, takes less parameter than other nature inspired algorithms, and, after some parameter tuning, yields very good results.

Cuckoo Search Clustering Algorithm: A novel strategy of biomimicry

In this paper, Human Visual System (HVS) characteristics are modeled using a Fuzzy Inference System (FIS) for robust image watermarking. The fuzzy input variables corresponding to luminance sensitivity, edge sensitivity computed using threshold and contrast sensitivity computed using variance are fed to a FIS driven by ten Fuzzy inference rules. The FIS produces a single output weighting factor which is used to embed a randomly generated normalized watermark with in the host image in the DCT domain. The signed image has good perceptual quality and is subject to StirMark image processing attacks. The high computed value of PSNR indicates robustness of the embedding algorithm. The watermark is extracted from the signed image using famous Cox's algorithm. The embedded and recovered watermarks are used to compute SIM(X, X∗) correlation parameter which shows that the extraction process is quite successful and the algorithm has good practical applications.

Arpita Sharma

Papers

Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm

Gray-scale image watermarking using GA-BPN hybrid network

A novel gray-scale image watermarking using hybrid Fuzzy-BPN architecture

Cuckoo Search Clustering Algorithm: A novel strategy of biomimicry

Digital image watermarking in DCT domain using Fuzzy Inference System