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Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 05 Issue: 01 June 2016, Page No. 50- 54
ISSN: 2278-2397
50
A Survey on Selected Algorithm –Evolutionary, Deep
Learning, Extreme Learning Machine
M. Sornam,R. Shalini
Dept of Computer Science, University of Madras, Chepauk, Chenna
Email: madasamy.sornam@gmail.com, rshalini1990@gmail.com
Abstract — the aim of this paper is to discuss the concept of
the evolutionary and machine learning strategies. The genetic
Algorithm is a satisfactory procedure for training multi-layer
Feed Forward Neural Networks. The MLFF neural network is
a one way signal made of multi layers which has the best trade-
off between speed and accuracy. It also deals with pattern
classification problems. The RSM predicts the mass transfer
parameters which are compared with capabilities of ANN
model. To assist the network to perform efficiently the learning
mechanisms DL and ELM have been implemented for
knowledge accuracy. This paper summarizes Multi-layer feed
forward (MFNN) using recent evolutionary Algorithms (GA),
Response surface methodology (RSM) for predicting the actual
capability of the network and to assist the dataset modulation
learning mechanisms like Deep Learning (DL), Extreme
Learning Machine(ELM) were used.
Keywords — Multi-layer Feed Forward Network (MLFN);
Genetic Algorithm (GA); Deep learning (DL); Extreme
learning machine (ELM); Resource surface methodology
(RSM).
I. INTRODUCTION
Artificial Neural Network (ANN) is an information processing
system that is inspired by the way human brain process
information. ANNs learn by example like people. Hence ANNs
are trained rather than programmed. The evolutionary
computing has become the most challenging deal in current
technology.
II. RELATED WORK
A) CLASSIFICATION:
A new regularizing technique called hyper-spectral reflectance
imaging of 400–1000 nm is used to detect chill damage in
peaches. Peach quality factors, such as cold injury (CI),
firmness, soluble solids content (SSC), extractable juice,
titratable acidity (TA) and chlorophyll content changed with
the development of cold injury during post-harvest storage.
When the fruit is stored at 0 _C the quality seems better than
when it is stored 5 _C. Based on the Cold Injury index, peaches
stored at 5 _C for 1 week and those at 0 _C for 2 weeks
remained normal, while peaches in other groups became cold-
injured. Eight optimal wavelengths (487, 514, 629, 656,774,
802, 920 and 948 nm) were determined by an artificial neural
network, according to normalized importance (NI) values of
each variable (wavelength).Hyper-spectral reflectance imaging
accurately identified cold-injured peaches during post-harvest
cold storage.
Hopfield networks are trained on noisy binary time series
obtained from recordings of spontaneous spiking cortical
activity using minimum probability flow (MPF). The
underlying parameter estimation algorithm is to minimize a
convex function over the data. After training, we find that the
number of memories in these networks is remarkably larger
than possible with fitting random patterns. Moreover, a robust
near-exact linear relationship between window size and
entropy exists. Hopfield networks could prove a powerful tool
for the efficient discovery of salient structure in noisy binary
data, including cortical spiking activity. In wireless
communication providing recommendations to mobile
operators about the activation or deactivation of base stations,
using data gathered from users’ traffic in base stations. The
system architecture for collecting necessary data from base
stations, and for running the machine learning algorithms in
order to provide recommendations to mobile operators about
base stations that can be activated or deactivated on specific
time periods of the day. Three different learning algorithms are
chosen based on neural networks, they are: Multilayer
Perceptron, SVM and PNN in which the PNN outperforms the
other two proposed algorithms, as it gives predictions with
high accuracy.
The process of silver cutting operation of bamboo is done
better with RSM model. There are numerous mathematical
models developed with dimensional analysis for different sizes
of split bamboo which can be effectively utilized for bamboo
processing operations. The computed selection of sliver
Cutting process parameters by dimensional analysis provides
effective guidelines to the manufacturing engineers so that they
can minimize E, Tp and tp for higher performances. Indian
industries use the data for calculating cutting forces and power
estimation for bamboo processing machines. RSM model can
be also utilized for estimation of maximum and minimum
values of response variables. To overcome the limitations that
exist in traditional K-NN, a novel method called G-KNN has
been introduced to improve the classification performance. K-
NN using Genetic Algorithm (GA) classifier is applied for
classification and similar k-neighbors are chosen at each
iteration process for classification by using GA, the test
samples are classified with these neighbors and the accuracy is
calculated for different number of K values to obtain high
accuracy; hence the computation time of K-NN is reduced
from the obtained results in this method. To carry out the
classification on CT lung image the MATLAB image
processing toolbox is well suitable.
B) EVOLUTIONARY:
K-means genetic algorithm is used for solving the multi
objective optimization problem. In contrast to conventional
multi-objective genetic algorithm, FS-NLMOGA maximizes
two objective functions also minimizes an objective function
simultaneously. These functions are optimized simultaneously
using feature selected criterion. Here the quality of the cluster
Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 05 Issue: 01 June 2016, Page No. 50- 54
ISSN: 2278-2397
51
is increased. There is no weighting parameters set for the
optimization. The cluster result shows higher accuracy than
NLMOGA. The performance of the proposed algorithm is
tested with several real life benchmark data sets. The results
indicate that the algorithm can simultaneously optimize the
chosen objectives by minimizing the intra-cluster distance and
maximizing the inter-cluster distance with high accuracy.
The ANN methodology has given the resultant PLN exhibiting
high drug loading efficiency ANN fits the experimental data
much better than RSM in this typical study attributable to ANN
capacity to accommodate more complex and non-linear
functional relationships. The combined application of factorial
design, ANN and continuous GA in modeling and multi-
objective optimization facilitates the growth of drug loading
with desired properties.
C) DEEP LEARNING:
Deep neural network frame work enables multimodal
integration learning of temporal sequences which includes
visual, auditory, and also motion. Two tasks utilizing a
humanoid robot in a real-world environment. The tasks
consisted of object manipulation and bell-ringing tasks.
Handling large amounts of training data with significant
dimensionality proves good scalability. There are three
applications of the acquired sensory motor integration model.
First, cross-modal memory retrieval was realized. Utilizing the
generalization capability of the deep auto encoder, our
proposed framework succeeded in retrieving temporal
sequences bi-directionally between image and motion. Second,
robust behavior recognition was realized by utilizing the
acquired multimodal features as inputs to supervised behavior
classification learning. Third, multimodal causality modeling
was realized.
A new possible architecture of self-learning and self-improving
capabilities for laser welding system has been introduced. It is
a simple form of industrially motivated intelligence. The
system has the capacity to improve its own performance, due
to the way that it optimizes the process in terms of goals rather
than in terms of mechanisms. It therefore promises to addresses
key requirements of modern industry, in that this architecture
combines fast learning with the capability to work well under
changing environments.
Deep neural networks are a powerful means of identifying
characteristic patterns in a variety of data types which are often
transferred over the Internet. This method can identify a
challenging variety of data formats without numerous hand
engineered features, while automatically adapting to new
formats. The power spectral density and byte distribution
capture the most variation between file types, and are superior
to sliding window entropy. Combining these representations
with sliding window entropy results in the most accurate and
consistent classifier, with average 97.44% accuracy amongst
several file types.
Deep architectures are suitable for solving complex problem
statements in every day-business, as it out performed MLPs
without pre-classification by RBMs, thus purely relying on
learning strategies and intelligent weight initialization.
Therefore, also complex ANN structures do not longer belong
exceptionally to the scientific domain and will find application
in data mining solutions in the near future. The DB-like ANN
not only learned the presented test problem more accurately
and in shorter time. Most importantly, the DBN-like approach
outperformed any other approach in verification as the
classification success on new datasets showed 100 % accuracy
in the tests.
D) EXTREME LEARNING MACHINE:
An efficient learning mechanism for establishing advances in
Big Data, Social Networking and Machine Learning and the
various ways in which they converge with FR. We proposed a
novel FR approach that can be more robust than the traditional
non-cloud based mechanisms, due to the accuracy boost and
performance improvement it can garner from the various
properties tendered by the aforementioned systems. The FR
framework was demonstrated for the task of Face Tagging in
social networking systems operating on Big Data by employing
the highly potent Extreme Learning Machines technique.
US-ELM algorithm performs better than other clustering
algorithms on big data when tested on multiple datasets. The
three distributed implementations of US-ELM lead to faster
training times than the serial implementation, making them
suitable for big data problems. The clustering performances
were comparable and the choice of the most suitable algorithm
was database dependent. Future enhancement can parallelize
the matrix operations of the algorithm as well as investigate
other clustering algorithms than k-means, FCM and GG.
ELM-LRF is a general framework for generic object
recognition. Distinct merits exist for ELM-LRF compared with
traditional methods. It is an efficient method for calculating
connection weights. In the experiments, the general framework
of ELM-LRF is evaluated on several generic object recognition
tasks. And it presents superior accuracy with exceptionally
high speed.
Training the Extreme Learning Machine based Single hidden
Layer Feed forward neural network. The standard
regularization approach minimizing the (Frobenius) norm of
the network output weights corresponds to a uniform
regularization of the ELM space dimensions and that the
proposed regularization approach is able to appropriately
weight the dimensions of the ELM space. The performance of
can be tested in small, medium and large-scale face recognition
problems in both closed and open-universe settings, where a
satisfactory performance can be achieved without requiring
additional costs in both training and test phases.
Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 05 Issue: 01 June 2016, Page No. 50- 54
ISSN: 2278-2397
52
TABLEI.
S. No
Comparison of different techniques with Evolutionary Strategies
AUTHOR YEAR CLASS MODEL AND
CLASSIFICATION
ALGORITHM
LEARNING MECHANISMS
9 Yara Rizk 2015 Extreme learning machine(ELM)
algorithm
Unsupervised machine learning
10 Yongqiang Li 2015 Genetic Algorithm(GA), Response
surface methodology(RSM)
Supervised machine learning
11 ZuoBai 2015 Back-propagation
(BP) algorithm
Extreme Learning Machine (ELM)
12 Florian Neukart 2014 SHOCID's Deep Learning, Boltzmann
Machine Learning.
13 Johannes Günther 2014 Deep learning algorithm, actor-critic
reinforcement learning (ACRL)
algorithm.
Deep learning, reinforcement
learning,
4 Kuniaki Noda 2014 Hessian-free algorithm, Deep
learning algorithm.
Deep learning, Multimodal
integration learning.
15 Sakhale.C.N 2014 Response Surface Methodology Experimental based Learning
TABLE II.
Reference
Comparison of different techniques with Evolutionary Strategies
SALIENT FEATURES ANALYSIS
1 The prediction of quality parameters by using
MLPANN models
performed well
Examining the peach quality in cold storage using hyper
spectral imaging system(spectroscopy + digital imaging
2 To appropriately weight the dimensions of the
ELM space
Unconstrained face recognition problems can be easily
solved.
3 The quality of the cluster is increased. Optimizes
several objectives simultaneously.
Clustering show higher accuracy in multi objective
optimization.
4 The computation time of K-NN is reduced from
the obtained results in this method
The filtered image is given to K-NN which is optimized
by GA
5 The technique is completely deterministic. Denoising the data by grouping similar patterns in
which eliminating patterns that occurs rarely.
6 Identifying a challenging variety of data formats
without numerous hand engineered features
Recent cyber security process of identifying patterns is
a challenging task it is done successfully using deep
neural network
S. No
Comparison of different techniques with Evolutionary Strategies
AUTHOR YEAR CLASS MODEL AND
CLASSIFICATION
ALGORITHM
LEARNING MECHANISMS
1 Leiqing Pan 2016 MPLANN model Supervised learning
2
Alexandros Iosifidis
2015 K-means and
ELM Algorithm
Extreme Learning Machine
3
Anusha.M 2015 K-Nearest Neighbor algorithm (K-
NN), Genetic Algorithm (GA)
Supervised machine learning
4 Bhuvaneswari.P 2015 K-Nearest Neighbor algorithm (K-
NN), Genetic Algorithm (GA)
Supervised machine learning
5 Christopher Hillar 2015 Lenz-Ising model
maximum entropy model
Boltzmann machine
Learning, Hopfield learning
Mechanism
6 Jonathan A. Cox 2015 Deep learning algorithms Deep learning
Mechanism
7 Pavlos Kosmides 2015 SVM learning algorithm Multilayer Perceptron, Probabilistic
Neural Network (PNN)
8 Vinay A 2015 Deep Learning Algorithm, Extreme
Learning Machines, K-Nearest.
Animetrics, BioID
Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 05 Issue: 01 June 2016, Page No. 50- 54
ISSN: 2278-2397
53
Reference
Comparison of different techniques with Evolutionary Strategies
SALIENT FEATURES ANALYSIS
7 Activation or deactivations of base stations are
easily done.
The growth in base stations of wireless communication
is harmful so undergoing dynamic activation and
deactivation of BSs.
8 Layer Fuzzy Neural Network (SLFNN)very
efficiently used in a linear system
ELM is well suitable for manipulating N samples with
minimum error.
Reference
Comparison of different techniques with Evolutionary Strategies
SALIENT FEATURES ANALYSIS
9 US-ELM lead to faster training times by using distributed
implementation than the serial implementation.
For big data analysis US-ELM achieves
faster training times rather than other
learning algorithms.
10 The resultant PLN exhibited high drug loading efficiency
(92%)
ANN model exhibits recognition and
generalization capability of drug loading
efficiency better than RSM model.
11 Comparing to CNN, it significantly reduces computational
complexity and requirement for huge training set.
Classifying an unknown object without
human intervention using ELM
12 Suitable for solving complex problem statements in every
day-business
Like data mining deep networks extract
knowledge using SHOCID.
13 The system has the capacity to improve its own
performance, due to the way that it optimizes the process
in terms of goals rather than in terms of mechanisms.
Implementing the idea of self-learning and
self-improving to manage the control
difficulties in laser welding systems.
14 Retrieving temporal sequences bi-directionally between
image and motion.
Well used for both robot motion and image
retrieval.
15 Can minimize E, Tp and tp for higher performances. The process of silver cutting of bamboo
utilizes RSM for better performance than
ANN
III. CONCLUSION
This survey paper is all about the evolution of the Genetic
algorithm which is a supervised learning method to train the
ANN. It is known for its mathematical simplicity and accuracy.
The genetic algorithm is the principal procedure for training
multi-layer Feed Forward Neural Networks. This genetic
algorithm has been proposed to find optimal parameter status.
The MLFF neural network is a one way signal made of multi
layers which has the best trade-off between speed and
accuracy. It also deals with pattern classification problems. The
RSM predicts the mass transfer parameters which are
compared with capabilities of ANN model.
References
[1] Alexandros Iosifidis, Anastasios Tefas and Ioannis Pitas
“Regularized Extreme Learning Machine for large-scale
media content analysis,” International Conference on
Information and Communication Technologies (ICICT),
pp. 420–427, 2015.
[2] M.Anusha, Dr.J.G.R.Sathiaseelan “Feature Selection using
K-Means Genetic Algorithm for Multi-objective
Optimization,”3rd International Conference on Recent
Trends in Computing, pp. 1074 – 1080, 2015.
[3] P.Bhuvaneswari, Dr. A. Brintha Therese “Detection of
Cancer in Lung with K-NN Classification Using Genetic
Algorithm,”2nd International Conference on
Nanomaterials and Technologies, pp. 433 – 440, 2015.
[4] Christopher Hillar and Felix Effenberger “Robust
discovery of temporal structure in multi-neuron recordings
using Hopfield networks,” International Conference on
Big Data, pp. 365–374, 2015.
[5] Florian Neukart, Sorin-Aurel Moraru “A Machine
Learning Approach for Abstraction Based on the Idea of
Deep Belief Artificial Neural Networks,”24th DAAAM
International Symposium on Intelligent Manufacturing and
Automation, pp. 1499 – 1508, 2014.
[6] Johannes Günther, Patrick M. Pilarski, Gerhard Helfrich,
Hao Shen, Klaus Diepold “First steps towards an
intelligent laser welding architecture using deep neural
networks and reinforcement learning,”2nd International
Conference on System-Integrated Intelligence: Challenges
for Product and Production Engineering, pp. 474 – 483,
2014.
[7] Jonathan A. Cox, Conrad D. James, James B. Aimone“A
Signal Processing Approach for Cyber Data Classification
with Deep Neural Networks,”Conference Organized by
Missouri University of Science and Technology , pp. 349
– 354, 2015.
[8] Kuniaki Noda, Hiroaki Arie, Yuki Suga, Tetsuya Ogata
“Multimodal integration learning of robot behavior using
deep neural networks,” Robotics and Autonomous
Systems, pp. 721 – 736, 2014.
[9] Leiqing Pan , Qiang Zhang, Wei Zhang, Ye Sun,
Pengcheng Hu, Kang Tu “Detection of cold injury in
peaches by hyperspectral reflectance imaging and artificial
neural network,” Food Chemistry 192, pp. 134 – 141,
2016.
[10] Pavlos Kosmidesa, Evgenia Adamopouloua, Konstantinos
Demestichasa, Miltiades Anagnostoua, Angelos Rouskasb
“On Intelligent Base Station Activation for Next
Generation Wireless Networks,”The 6th International
Conference on Emerging Ubiquitous Systems and
Pervasive Networks, pp. 82 – 88, 2015.
Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 05 Issue: 01 June 2016, Page No. 50- 54
ISSN: 2278-2397
54
[11] C.N. Sakhale, S.N. Waghmare, S.K. undirwade, V.M.
Sonde, M.P. Singh“Formulation and Comparison of
Experimental based Mathematical Model with Artificial
Neural Network Simulation and RSM (Response Surface
Methodology) Model for Optimal Performance of Sliver
Cutting Operation of Bamboo,”3rd International
Conference on Materials Processing and Characterisation
(ICMPC 2014), pp. 877 – 891, 2014.
[12] A Vinay,Vinay S Shekhar,J Rituparna , Tushar Aggrawal,
K N Balasubramanya Murthy, S Natarajan“Cloud Based
Big Data Analytics Framework for Face Recognition in
Social Networks using Machine Learning,”2nd
International Symposium on Big Data and Cloud
Computing (ISBCC’15), pp. 623 – 630, 2015.
[13] Yara Rizk and Mariette Awad “On the Distributed
Implementation of Unsupervised Extreme Learning
Machines for Big Data,”International Conference on Big
Data, pp. 167–174, 2015.
[14] Yongqiang Li , Mohammadreza R. Abbaspour, Paul V.
Grootendorst, Andrew M. Rauth,Xiao Yu Wu,
“Optimization of controlled release nanoparticle
formulation of verapamil hydrochloride using artificial
neural networks with genetic algorithm and response
surface methodology,” European Journal of Pharmaceutics
and Biopharmaceutics, pp. 170-179, 2015.
Zuo Bai, Liyanaarachchi Lekamalage Chamara Kasun and
Guang-Bin Huang,“Generic Object Recognition with
Local Receptive Fields Based Extreme Learning
Machine,” International Conference on Big Data, pp. 391–
399, 2015.

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A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning Machine

  • 1. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 05 Issue: 01 June 2016, Page No. 50- 54 ISSN: 2278-2397 50 A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning Machine M. Sornam,R. Shalini Dept of Computer Science, University of Madras, Chepauk, Chenna Email: madasamy.sornam@gmail.com, rshalini1990@gmail.com Abstract — the aim of this paper is to discuss the concept of the evolutionary and machine learning strategies. The genetic Algorithm is a satisfactory procedure for training multi-layer Feed Forward Neural Networks. The MLFF neural network is a one way signal made of multi layers which has the best trade- off between speed and accuracy. It also deals with pattern classification problems. The RSM predicts the mass transfer parameters which are compared with capabilities of ANN model. To assist the network to perform efficiently the learning mechanisms DL and ELM have been implemented for knowledge accuracy. This paper summarizes Multi-layer feed forward (MFNN) using recent evolutionary Algorithms (GA), Response surface methodology (RSM) for predicting the actual capability of the network and to assist the dataset modulation learning mechanisms like Deep Learning (DL), Extreme Learning Machine(ELM) were used. Keywords — Multi-layer Feed Forward Network (MLFN); Genetic Algorithm (GA); Deep learning (DL); Extreme learning machine (ELM); Resource surface methodology (RSM). I. INTRODUCTION Artificial Neural Network (ANN) is an information processing system that is inspired by the way human brain process information. ANNs learn by example like people. Hence ANNs are trained rather than programmed. The evolutionary computing has become the most challenging deal in current technology. II. RELATED WORK A) CLASSIFICATION: A new regularizing technique called hyper-spectral reflectance imaging of 400–1000 nm is used to detect chill damage in peaches. Peach quality factors, such as cold injury (CI), firmness, soluble solids content (SSC), extractable juice, titratable acidity (TA) and chlorophyll content changed with the development of cold injury during post-harvest storage. When the fruit is stored at 0 _C the quality seems better than when it is stored 5 _C. Based on the Cold Injury index, peaches stored at 5 _C for 1 week and those at 0 _C for 2 weeks remained normal, while peaches in other groups became cold- injured. Eight optimal wavelengths (487, 514, 629, 656,774, 802, 920 and 948 nm) were determined by an artificial neural network, according to normalized importance (NI) values of each variable (wavelength).Hyper-spectral reflectance imaging accurately identified cold-injured peaches during post-harvest cold storage. Hopfield networks are trained on noisy binary time series obtained from recordings of spontaneous spiking cortical activity using minimum probability flow (MPF). The underlying parameter estimation algorithm is to minimize a convex function over the data. After training, we find that the number of memories in these networks is remarkably larger than possible with fitting random patterns. Moreover, a robust near-exact linear relationship between window size and entropy exists. Hopfield networks could prove a powerful tool for the efficient discovery of salient structure in noisy binary data, including cortical spiking activity. In wireless communication providing recommendations to mobile operators about the activation or deactivation of base stations, using data gathered from users’ traffic in base stations. The system architecture for collecting necessary data from base stations, and for running the machine learning algorithms in order to provide recommendations to mobile operators about base stations that can be activated or deactivated on specific time periods of the day. Three different learning algorithms are chosen based on neural networks, they are: Multilayer Perceptron, SVM and PNN in which the PNN outperforms the other two proposed algorithms, as it gives predictions with high accuracy. The process of silver cutting operation of bamboo is done better with RSM model. There are numerous mathematical models developed with dimensional analysis for different sizes of split bamboo which can be effectively utilized for bamboo processing operations. The computed selection of sliver Cutting process parameters by dimensional analysis provides effective guidelines to the manufacturing engineers so that they can minimize E, Tp and tp for higher performances. Indian industries use the data for calculating cutting forces and power estimation for bamboo processing machines. RSM model can be also utilized for estimation of maximum and minimum values of response variables. To overcome the limitations that exist in traditional K-NN, a novel method called G-KNN has been introduced to improve the classification performance. K- NN using Genetic Algorithm (GA) classifier is applied for classification and similar k-neighbors are chosen at each iteration process for classification by using GA, the test samples are classified with these neighbors and the accuracy is calculated for different number of K values to obtain high accuracy; hence the computation time of K-NN is reduced from the obtained results in this method. To carry out the classification on CT lung image the MATLAB image processing toolbox is well suitable. B) EVOLUTIONARY: K-means genetic algorithm is used for solving the multi objective optimization problem. In contrast to conventional multi-objective genetic algorithm, FS-NLMOGA maximizes two objective functions also minimizes an objective function simultaneously. These functions are optimized simultaneously using feature selected criterion. Here the quality of the cluster
  • 2. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 05 Issue: 01 June 2016, Page No. 50- 54 ISSN: 2278-2397 51 is increased. There is no weighting parameters set for the optimization. The cluster result shows higher accuracy than NLMOGA. The performance of the proposed algorithm is tested with several real life benchmark data sets. The results indicate that the algorithm can simultaneously optimize the chosen objectives by minimizing the intra-cluster distance and maximizing the inter-cluster distance with high accuracy. The ANN methodology has given the resultant PLN exhibiting high drug loading efficiency ANN fits the experimental data much better than RSM in this typical study attributable to ANN capacity to accommodate more complex and non-linear functional relationships. The combined application of factorial design, ANN and continuous GA in modeling and multi- objective optimization facilitates the growth of drug loading with desired properties. C) DEEP LEARNING: Deep neural network frame work enables multimodal integration learning of temporal sequences which includes visual, auditory, and also motion. Two tasks utilizing a humanoid robot in a real-world environment. The tasks consisted of object manipulation and bell-ringing tasks. Handling large amounts of training data with significant dimensionality proves good scalability. There are three applications of the acquired sensory motor integration model. First, cross-modal memory retrieval was realized. Utilizing the generalization capability of the deep auto encoder, our proposed framework succeeded in retrieving temporal sequences bi-directionally between image and motion. Second, robust behavior recognition was realized by utilizing the acquired multimodal features as inputs to supervised behavior classification learning. Third, multimodal causality modeling was realized. A new possible architecture of self-learning and self-improving capabilities for laser welding system has been introduced. It is a simple form of industrially motivated intelligence. The system has the capacity to improve its own performance, due to the way that it optimizes the process in terms of goals rather than in terms of mechanisms. It therefore promises to addresses key requirements of modern industry, in that this architecture combines fast learning with the capability to work well under changing environments. Deep neural networks are a powerful means of identifying characteristic patterns in a variety of data types which are often transferred over the Internet. This method can identify a challenging variety of data formats without numerous hand engineered features, while automatically adapting to new formats. The power spectral density and byte distribution capture the most variation between file types, and are superior to sliding window entropy. Combining these representations with sliding window entropy results in the most accurate and consistent classifier, with average 97.44% accuracy amongst several file types. Deep architectures are suitable for solving complex problem statements in every day-business, as it out performed MLPs without pre-classification by RBMs, thus purely relying on learning strategies and intelligent weight initialization. Therefore, also complex ANN structures do not longer belong exceptionally to the scientific domain and will find application in data mining solutions in the near future. The DB-like ANN not only learned the presented test problem more accurately and in shorter time. Most importantly, the DBN-like approach outperformed any other approach in verification as the classification success on new datasets showed 100 % accuracy in the tests. D) EXTREME LEARNING MACHINE: An efficient learning mechanism for establishing advances in Big Data, Social Networking and Machine Learning and the various ways in which they converge with FR. We proposed a novel FR approach that can be more robust than the traditional non-cloud based mechanisms, due to the accuracy boost and performance improvement it can garner from the various properties tendered by the aforementioned systems. The FR framework was demonstrated for the task of Face Tagging in social networking systems operating on Big Data by employing the highly potent Extreme Learning Machines technique. US-ELM algorithm performs better than other clustering algorithms on big data when tested on multiple datasets. The three distributed implementations of US-ELM lead to faster training times than the serial implementation, making them suitable for big data problems. The clustering performances were comparable and the choice of the most suitable algorithm was database dependent. Future enhancement can parallelize the matrix operations of the algorithm as well as investigate other clustering algorithms than k-means, FCM and GG. ELM-LRF is a general framework for generic object recognition. Distinct merits exist for ELM-LRF compared with traditional methods. It is an efficient method for calculating connection weights. In the experiments, the general framework of ELM-LRF is evaluated on several generic object recognition tasks. And it presents superior accuracy with exceptionally high speed. Training the Extreme Learning Machine based Single hidden Layer Feed forward neural network. The standard regularization approach minimizing the (Frobenius) norm of the network output weights corresponds to a uniform regularization of the ELM space dimensions and that the proposed regularization approach is able to appropriately weight the dimensions of the ELM space. The performance of can be tested in small, medium and large-scale face recognition problems in both closed and open-universe settings, where a satisfactory performance can be achieved without requiring additional costs in both training and test phases.
  • 3. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 05 Issue: 01 June 2016, Page No. 50- 54 ISSN: 2278-2397 52 TABLEI. S. No Comparison of different techniques with Evolutionary Strategies AUTHOR YEAR CLASS MODEL AND CLASSIFICATION ALGORITHM LEARNING MECHANISMS 9 Yara Rizk 2015 Extreme learning machine(ELM) algorithm Unsupervised machine learning 10 Yongqiang Li 2015 Genetic Algorithm(GA), Response surface methodology(RSM) Supervised machine learning 11 ZuoBai 2015 Back-propagation (BP) algorithm Extreme Learning Machine (ELM) 12 Florian Neukart 2014 SHOCID's Deep Learning, Boltzmann Machine Learning. 13 Johannes Günther 2014 Deep learning algorithm, actor-critic reinforcement learning (ACRL) algorithm. Deep learning, reinforcement learning, 4 Kuniaki Noda 2014 Hessian-free algorithm, Deep learning algorithm. Deep learning, Multimodal integration learning. 15 Sakhale.C.N 2014 Response Surface Methodology Experimental based Learning TABLE II. Reference Comparison of different techniques with Evolutionary Strategies SALIENT FEATURES ANALYSIS 1 The prediction of quality parameters by using MLPANN models performed well Examining the peach quality in cold storage using hyper spectral imaging system(spectroscopy + digital imaging 2 To appropriately weight the dimensions of the ELM space Unconstrained face recognition problems can be easily solved. 3 The quality of the cluster is increased. Optimizes several objectives simultaneously. Clustering show higher accuracy in multi objective optimization. 4 The computation time of K-NN is reduced from the obtained results in this method The filtered image is given to K-NN which is optimized by GA 5 The technique is completely deterministic. Denoising the data by grouping similar patterns in which eliminating patterns that occurs rarely. 6 Identifying a challenging variety of data formats without numerous hand engineered features Recent cyber security process of identifying patterns is a challenging task it is done successfully using deep neural network S. No Comparison of different techniques with Evolutionary Strategies AUTHOR YEAR CLASS MODEL AND CLASSIFICATION ALGORITHM LEARNING MECHANISMS 1 Leiqing Pan 2016 MPLANN model Supervised learning 2 Alexandros Iosifidis 2015 K-means and ELM Algorithm Extreme Learning Machine 3 Anusha.M 2015 K-Nearest Neighbor algorithm (K- NN), Genetic Algorithm (GA) Supervised machine learning 4 Bhuvaneswari.P 2015 K-Nearest Neighbor algorithm (K- NN), Genetic Algorithm (GA) Supervised machine learning 5 Christopher Hillar 2015 Lenz-Ising model maximum entropy model Boltzmann machine Learning, Hopfield learning Mechanism 6 Jonathan A. Cox 2015 Deep learning algorithms Deep learning Mechanism 7 Pavlos Kosmides 2015 SVM learning algorithm Multilayer Perceptron, Probabilistic Neural Network (PNN) 8 Vinay A 2015 Deep Learning Algorithm, Extreme Learning Machines, K-Nearest. Animetrics, BioID
  • 4. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 05 Issue: 01 June 2016, Page No. 50- 54 ISSN: 2278-2397 53 Reference Comparison of different techniques with Evolutionary Strategies SALIENT FEATURES ANALYSIS 7 Activation or deactivations of base stations are easily done. The growth in base stations of wireless communication is harmful so undergoing dynamic activation and deactivation of BSs. 8 Layer Fuzzy Neural Network (SLFNN)very efficiently used in a linear system ELM is well suitable for manipulating N samples with minimum error. Reference Comparison of different techniques with Evolutionary Strategies SALIENT FEATURES ANALYSIS 9 US-ELM lead to faster training times by using distributed implementation than the serial implementation. For big data analysis US-ELM achieves faster training times rather than other learning algorithms. 10 The resultant PLN exhibited high drug loading efficiency (92%) ANN model exhibits recognition and generalization capability of drug loading efficiency better than RSM model. 11 Comparing to CNN, it significantly reduces computational complexity and requirement for huge training set. Classifying an unknown object without human intervention using ELM 12 Suitable for solving complex problem statements in every day-business Like data mining deep networks extract knowledge using SHOCID. 13 The system has the capacity to improve its own performance, due to the way that it optimizes the process in terms of goals rather than in terms of mechanisms. Implementing the idea of self-learning and self-improving to manage the control difficulties in laser welding systems. 14 Retrieving temporal sequences bi-directionally between image and motion. Well used for both robot motion and image retrieval. 15 Can minimize E, Tp and tp for higher performances. The process of silver cutting of bamboo utilizes RSM for better performance than ANN III. CONCLUSION This survey paper is all about the evolution of the Genetic algorithm which is a supervised learning method to train the ANN. It is known for its mathematical simplicity and accuracy. The genetic algorithm is the principal procedure for training multi-layer Feed Forward Neural Networks. This genetic algorithm has been proposed to find optimal parameter status. The MLFF neural network is a one way signal made of multi layers which has the best trade-off between speed and accuracy. It also deals with pattern classification problems. The RSM predicts the mass transfer parameters which are compared with capabilities of ANN model. References [1] Alexandros Iosifidis, Anastasios Tefas and Ioannis Pitas “Regularized Extreme Learning Machine for large-scale media content analysis,” International Conference on Information and Communication Technologies (ICICT), pp. 420–427, 2015. [2] M.Anusha, Dr.J.G.R.Sathiaseelan “Feature Selection using K-Means Genetic Algorithm for Multi-objective Optimization,”3rd International Conference on Recent Trends in Computing, pp. 1074 – 1080, 2015. [3] P.Bhuvaneswari, Dr. A. Brintha Therese “Detection of Cancer in Lung with K-NN Classification Using Genetic Algorithm,”2nd International Conference on Nanomaterials and Technologies, pp. 433 – 440, 2015. [4] Christopher Hillar and Felix Effenberger “Robust discovery of temporal structure in multi-neuron recordings using Hopfield networks,” International Conference on Big Data, pp. 365–374, 2015. [5] Florian Neukart, Sorin-Aurel Moraru “A Machine Learning Approach for Abstraction Based on the Idea of Deep Belief Artificial Neural Networks,”24th DAAAM International Symposium on Intelligent Manufacturing and Automation, pp. 1499 – 1508, 2014. [6] Johannes Günther, Patrick M. Pilarski, Gerhard Helfrich, Hao Shen, Klaus Diepold “First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning,”2nd International Conference on System-Integrated Intelligence: Challenges for Product and Production Engineering, pp. 474 – 483, 2014. [7] Jonathan A. Cox, Conrad D. James, James B. Aimone“A Signal Processing Approach for Cyber Data Classification with Deep Neural Networks,”Conference Organized by Missouri University of Science and Technology , pp. 349 – 354, 2015. [8] Kuniaki Noda, Hiroaki Arie, Yuki Suga, Tetsuya Ogata “Multimodal integration learning of robot behavior using deep neural networks,” Robotics and Autonomous Systems, pp. 721 – 736, 2014. [9] Leiqing Pan , Qiang Zhang, Wei Zhang, Ye Sun, Pengcheng Hu, Kang Tu “Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network,” Food Chemistry 192, pp. 134 – 141, 2016. [10] Pavlos Kosmidesa, Evgenia Adamopouloua, Konstantinos Demestichasa, Miltiades Anagnostoua, Angelos Rouskasb “On Intelligent Base Station Activation for Next Generation Wireless Networks,”The 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, pp. 82 – 88, 2015.
  • 5. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 05 Issue: 01 June 2016, Page No. 50- 54 ISSN: 2278-2397 54 [11] C.N. Sakhale, S.N. Waghmare, S.K. undirwade, V.M. Sonde, M.P. Singh“Formulation and Comparison of Experimental based Mathematical Model with Artificial Neural Network Simulation and RSM (Response Surface Methodology) Model for Optimal Performance of Sliver Cutting Operation of Bamboo,”3rd International Conference on Materials Processing and Characterisation (ICMPC 2014), pp. 877 – 891, 2014. [12] A Vinay,Vinay S Shekhar,J Rituparna , Tushar Aggrawal, K N Balasubramanya Murthy, S Natarajan“Cloud Based Big Data Analytics Framework for Face Recognition in Social Networks using Machine Learning,”2nd International Symposium on Big Data and Cloud Computing (ISBCC’15), pp. 623 – 630, 2015. [13] Yara Rizk and Mariette Awad “On the Distributed Implementation of Unsupervised Extreme Learning Machines for Big Data,”International Conference on Big Data, pp. 167–174, 2015. [14] Yongqiang Li , Mohammadreza R. Abbaspour, Paul V. Grootendorst, Andrew M. Rauth,Xiao Yu Wu, “Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology,” European Journal of Pharmaceutics and Biopharmaceutics, pp. 170-179, 2015. Zuo Bai, Liyanaarachchi Lekamalage Chamara Kasun and Guang-Bin Huang,“Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine,” International Conference on Big Data, pp. 391– 399, 2015.