Gait Using Moment With Gray And Silhouette Image

Jyoti Bharti1, Navneet Manjhi1, M.K.Gupta2 and Bimi jain2

1Department of Computer Science and Engineering, M.A.N.I.T, Bhopal, India

2Department of Electronics and Communication, M.A.N.I.T, Bhopal, India

ABSTRACT

A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of Gray Scale and Silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and Nearest Neighbor Classifier are used for classification. Both achieved higher recognition.

KEYWORDS

Gait, Fuzzy Logic, Nearest Neighbor (without fuzzy), Recognition Rate, Moments.

Original Source URL: https://airccse.org/journal/ijsc/papers/6115ijsc02.pdf

https://airccse.org/journal/ijsc/current2015.html

International Journal on Soft Computing ( IJSC )

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

https://airccse.org/journal/ijsc/ijsc.html

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Here's where you can reach us : ijscjournal@yahoo.com or ijsc@aircconline.com

Submission System

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#FuzzyLogic #NeuralComputing #evolutionarycomputation #machinelearning #patternrecognition

International Journal on Soft Computing ( IJSC )

 Call for Papers! November Issue!

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International Journal on Soft Computing ( IJSC )

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

https://airccse.org/journal/ijsc/ijsc.html

Contact Us 

Here's where you can reach us : ijscjournal@yahoo.com or ijsc@aircconline.com

Submission System

http://coneco2009.com/submissions/imagination/home.html

Submission Deadline : October 30, 2021

#AIRCC #ijsc #softcomputing #neuralnetworks #FuzzySystems #machinelearning #patternrecognition #wavelet #roughsets

Multispectral Image Analysis Using Random Forest

Barrett Lowe and Arun Kulkarni

Department of Computer Science, The University of Texas at Tyler

ABSTRACT

Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin 2001 for classification and clustering. Random Forest grows many decision trees for classification. To classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a classification. The forest chooses the classification having the most votes. Random Forest provides a robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral images using various classifiers such as the maximum likelihood classifier, neural network, support vector machine (SVM), and Random Forest and compare their results.

KEYWORDS

Classification, Decision Trees, Random Forest, Multispectral Images

Original Source URL: https://airccse.org/journal/ijsc/papers/6115ijsc01.pdf

https://airccse.org/journal/ijsc/current2015.html

Fuzzy Entropy Based Optimal Thresholding Technique for Image Enhancement

U.Sesadri1, B. Siva Sankar1, C. Nagaraju2, 

1Vaagdevi Institute of Technology and Science, India and 2Yogi Vemana University, India

ABSTRACT

Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.

KEY WORDS

fuzzy entropy, segmentation, soft computing, MAD and optimal thresholding

Original Source URL: https://airccse.org/journal/ijsc/papers/6215ijsc02.pdf

https://airccse.org/journal/ijsc/current2015.html

Fuzzy Applications in a Power Station

T.K Sai1 and K.A. Reddy2

1NTPC, India, 2KITSW, India

ABSTRACT

Power generation today is an increasingly demanding task, worldwide, because of emphasis on efficient ways of generation. A power station is a complicated multivariable controlled plant, which consists of boiler, turbine, generator, power network and loads. The power sector sustainability depends on innovative technology and practices in maintaining unit performance, operation, flexibility and availability . The demands being placed on Control & Instrumentation engineers include economic optimization, practical methods for adaptive and learning control, software tools that place state-of-art methods . As a result, Fuzzy techniques are explored which aim to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low cost. This paper proposes use of fuzzy techniques in two critical areas of Soot Blowing optimization and Drum Level Control. 

Presently, in most of the Power stations the soot blowing is done based on a fixed time schedule. In many instances, certain boiler stages are blown unnecessarily, resulting in efficiency loss. Therefore an fuzzy based system is proposed which shall indicate individual section cleanliness to determine correct soot blowing scheme. Practical soot blowing optimization improves boiler performance, reduces NOx emissions and minimizes disturbances caused by soot blower activation. Due to the dynamic behaviour of power plant, controlling the Drum Level is critical. If the level becomes too low, the boiler can run dry resulting in mechanical damage of the drum and boiler tubes. If the level becomes too high, water can be carried over into the Steam Turbine which shall result in catastrophic damage. Therefore an fuzzy based system is proposed to replace the existing conventional controllers

KEYWORDS

Artificial intelligence, Expert Systems, Fuzzy Logic, Power generation, Soot Blowers, Drum Level

Original Source URL: https://airccse.org/journal/ijsc/papers/6215ijsc01.pdf

https://airccse.org/journal/ijsc/current2015.html