JOURNAL OF CRITICAL REVIEWS

ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 AN EPITOME OF ALGORITHMS, FRAME WORKS AND ITS APPLICATIONS

G.Nirmala, Dr.K.K.Thygharajan 1Assistant Professor, RMD Engineering College, Kavaraipettai. 2Academic Dean, RMD Engineering College, Kavaraipettai.

Received: 28 February 2020 Revised and Accepted: 06 March 2020

ABSTRACT: is a super set of current technology comprising of machine learning in turn deep learning. A detailed analysis and assessment of deep learning framework and various application areas that is currently available with its strength and weakness was depicted . Deep learning is now days preferred by everyone due to its data science and computer vision capabilities. All these frameworks are free of cost and available as open source. In this paper the major areas of applications are elaborately discussed. A snapshot on various algorithms that are frequently used in deep learning is depicted. Deep learning frame works which are familiar are tabulated with its important characteristics.

KEYWORDS: Deep learning, Machine learning, CNNs, Frameworks.

I. INTRODUCTION

Deep learning is a current topic and need of the hour which has effective, supervised,time and cost efficient methods to be used in wide range of applications such as business,socialmedia,science and government. Machine learning makes the process of feature extraction, analysis and understanding of useful information from digital images very easy. Computer vision includes tasks like object recognition, classification, object detection and image segmentation.. It has been a great challenge to define recursive neural network, ,convolution neural network and unsupervised pre trained neural network. It plays a vital role in data science in the area of biological and medical science where there are oceans of data for analysis. Deep learning is a subset of machine learning. This review provides research directions to the fellow researchers who are interested to work in this field. This paper provides an insight into various algorithms used in deep learning and the fields where it is applied. [1] jaison bunk and etal detected a tampered region forgery using LSTMS to classify the location. Deep learning is the need of the hour. AthanasiosVoulodimos [2] and etal written a complete review on deep learning methods and developements in computer vision prespective.

II. PANAROMA ON DEEP LEARNING ALGORITHMS:

CNNs: Convolution Neural Network (CNN) was proposed in 1980 where convolution layer is the major building block in CNN of from the research of synchronous oscillation phenomenon in visual cortical neurons of cat in the case of external pulsing. even though it needs a tough job in estimating the optimum values for the large number of CNN parameters and finding out the optimal number of features to be generated, CNN has been widely used by researchers in image processing because the CNN does not require any training and it can

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ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 produce minimal number of features even for large images. The feature extraction based on CNN was used by many researchers to distinguish the regions of images which are invariant to scale, rotation [8].CNNs uses two type of approaches. using Machine learning algorithms for feature and diamensionality reduction like eigenvalues,Singlevaluedecomposition,PCA, Random forest, High correlation etc.., and classicalmethods.classical methods inturn has three main layers Convolution layer which uses filters to select edges, lines and various features from the given input image.these filters are called weights or parameters gives an array of numbers as output which is called feature map or activation map.second layer call ReLu which converts normalises the feature map by dividing with total number of pixels.It also converts negative value to 0 and makes the activation map all positve values . The function of the last layer called Maxpooling or average pooling layer is it takes input from feature map and find the average or maximum fetaureetection with minimum diamension.Asifullah Khan [4] etal presented CNN has automatic feature extraction capability.

RNNS:Recurrent neural networks is a classification of artificial neural networks which is used for text data in which the association between the nodes forms a directed graph with a temporal sequence by applying recursive set of weights. It takes the input as sequence and gives output also as a sequence. It is mainly used for implementing natural language processing and language translations.It takes the input at each step and also previous step ouput like feedbackwith a small memory.Wenguanwang [5] and etal proved a novel deep learingbased salient object detection algorithm with cross data sets and evaluation results. Yan lechun and etal [6] reviewed and given a clear insight of deep learingalgorithms .

LSTMS: Long Short Term Memory Networks abbreviated as LSTMs introduced in 1991 by Hochreiter [16] was more innovative, efficient and gradient based method. It is local in space and time and its time complexity is 0(1).It has three model of converting from vector to sequence model where the input is a image as input and output is numeric array as sequence in image captioning,changing from sequence to vector model sentiment analysis where input is a word and output is vector.third model is sequence to sequence in which both input and output is word used in natural language processing.It has a cell state factor representing from ct-1 to ct.It comprises of three gates input gate, forget gate and output gate. Finally distributes the gradient.It is much complicated . It is changed to advanced version with only two gate reset gate and update gate called as GRU gated recurrent unit.

AutoEncoders: Auto encoders are conversion algorithms which compress the given data input sequence to another sequence again revert back to the original given input as ouput. it encodes the given text to encrypted format and again decrypts or decodes back to the input.the intermediate represenation with minimal code is called latent-space representation. Aditya Khamparia [7] briefly in his survey explained the processing of auto encoders for better results the number of hidden layers and introduced with sparsity constraints. Shetra [8] and etal tabulated implemeted models of autoencoders are two types variational and sparse samples commonly 1344 JOURNAL OF CRITICAL REVIEWS

ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 applied for diamentionality reduction and encoding in unsupervised learning.

DBN: In Deep Belief Networks every two consecutive layers form a RBM Restricted BoltzmanMachine.Ghasemi[20] proposed DBF are more popular and giving more accuracy like deep neural networks.It can be trained in a greedy and a pictorial representation for extracting deep represenationaldata.It has layer wise pretraing as first step and then final tuning stage.

frame works plays a main role in solving business challenges. AI rightly partnered with deep learning framework completed the task very effectively and efficiently. Researchers according to the requirements can choose the type of particular frameworks. All these frameworks are free of cost and available as open source to cater the faster need of wide range of developers and researchers. Some of the software frame works for deep learning are Tensorflow, keras, cafee, pytorch, microsoft cognitive toolkit.MXNet,DL4j are to be discussed in Table1 here.

Name of Speciality Multiple the Frame Languages Developer Deep GPU work Learning Support Architecture Supported Tensorflow Python Google CNN,RNN, Multiple level of Yes C++ LSTMs... Abstraction in ML pipeline keras Python Google CNN,RNN Dropout,batch GPUS and Network normalization, TPUs library pooling caffe C++ Berleley CNN Speed, CPU to with python AI robustness, GPU Interface Researrch modularity Moderation pytorch Python Facebook CNN,RNN Numpy, yes C++ AI Automated cuda Research Differentiation lab and Microsoft microsoft C++ Microsoft yes Distributed deep Multiple cognitive Research learning GPUS and toolkit Stochastic servers gradient decent MXNet C++,Matlab,R, Apache CNN,RNN, Cloud, No Python,Julia, LSTMs... faster Learning scala, rate

DL4j Java,Scala Apache RNN,LSTMs, Effective library Supports DL4j Cuda RBM,DBM for Linear Multiple python Algebra an GPUS d Matrix Manupulations

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ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 Contributions in github contributions as on 10/5/2020

Tensor keras caffe pytorch microsoft MXNet DL4j flow cognitive toolkit 2485 816 265 1395 199 792 37

Self-Driven cars: Deep learning algorithm RNNis used to train the automated human less cars by many trials and Convulsion neuralnetwork is applied for objcetidentification.Train an end to end deep learning model for an automated car with a driving simulator is sucessfullylaunced. Game Playing:Deep learning algorithms have been applied in video games like arcade games,firts-person shooters.Deep Q-learning algorithm for playing snamk game and deep reinforementleaarning algorithm using keras and tensorflow for handling extreme large decision spaces and sparse rewards. Health care: Health care industry cover many hidden openings and similarites in testing clinical data which inturn helps doctors to treat thier patients effectively.There are three important applications in Health care. First one is discovery of medicines and drugs.Deeplearning is very much useful in the discovery of medicines and

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ISSN- 2394-5125 VOL 7, ISSUE 05, 2020 thier progress which works on the analysis and evaluation of past medical history and provides efficient treatment.secondly in medical imaging emthods like MRI,CTScans,EEG,ECGs which will diagnose harmful diseases. This deep learning assis the doctors to analyse better and provide effective treatment.Lastly in genome analysis this deep learning algorithms are implemted in predicting the effect and symptoms of diseases.Recently analysis for COVID19 using deep learning algorithms are under process. Fraud Detection: detecting financial fraud is a very complicated job,there are various number of methods to detect them but they require more time.deep learning methods can be used in the application areas like credit card fraud detection,telecommuniction fraud detection,online auction fraud detection and computer intrusion detection . Deep learing algorithms auto encoder and CNNs are used to solve this problem [17] Earthquakes: Deep learning is more useful and sucessful in handling complex data of earth quake analysis like seismic waveform for effective event detection. It is very expensive for compution of viscoelastic data to detrmine the earthquake timing but very much useful in saving life of people as discovered by Harvard scientist.some of the applications are Deep detect,PhaseNet, CRED or Phase Link.with the architectures of CNNS and RNNS we can automatically choose seismic waves and predict foreshock,main shock and aftershock ocuuring in earthquake. Finance: In financial world artificial Intelligences is very much useful in analysing and forecasting the stock market strategy,predicting corporate bakruptcy and overall fincancialsystem.the natural inclination of ANNS can interpret and predict patterns in complex massive market data. Every day if a new data is recorded then Deep learning systems with its techniques like random forest, Support vector machines, Decision trees learns its sucesses or failures and adjust itself automatically and have a good performance. Image recognition: Deep learning applications are very famous in the fields like image classification, recognition,caption generation and Image description.Deeplearning not only classifies the labelled images but also it captions and describes the images in simple english.Anoval method using mutilmodal RNN which is a fusion of CNN and birectional RNN gives a good performance in image description.(Karpathy&Fei-Fei 2017). Marketing and Forecasting: Deep learning plays a consistent role in digital marketing and forecasting since it has the efficiency to handle large,complex,unstructureddata.Even big technology giants like Google,Facebook, Amazon are using lot of deep learning methods to optimisemarketing.Various applications like predicive targeting, lead scoring, customer life value forecasting and recomendations are developed for clear future estimations in sales and CRM metrics designing. Election prediction: Deep learning algorithms are very much useful in prediction of election results based on the vote. Machine learning has a very note worthyprogess in the recent years and its learning models are applied to find the chances of victory in the election based on supporter or voter opinions on the social media. voice Search: speech recognition makes our home automated.Large-scale Automated Speech recogntion is the great sucess based on deep learning architectures like LSTM,RNNS,AI Coupled with Natural language processing,Machine Learning and voide search plays a vital role in many challegingapplications.some of the automated Speech recognition softwares like Amazon Alexa,MicrosoftCortana,Google Now are developed using the integerated deep learning models like LSTS,CNNs and DNNS. MachineTranslation: In olden days it is a great glich involving large statistical models in machine translation.LSTMS have significant role in Machine translation,Naturallangague processing and modeling a language.with the intervention of deep learning algorithms like Auto encoders and RNNs, it is effectively implemented and accurately translated with very less translation error rate.Google Translate is a leading product available in industry. Advertising: AI plays a vital role in todays digital marketing which handles fast moving huge amount of data. The deep learning algorithms detcct the unfavouarble market situation and make the decision to incurr the unxcepted loss. Simple example of making a personal reminder on date birth of every customer makes the special advertising. Googles deep Mind and lip read TV are implemented sucessfully.

III. CONCLUSION: In this paper we presented vital deep learning algorithms and frameworks.Research in deeplearning is the cuurent need as its progressing in a faster rate.Frameworks currently existing are competent in their capability.Factors for selecting the approriate algorithms and frame work based on the application areas like robustness,flexibility,GPUcapabilities,modularity are to be considered greatly.In future we will explore the ways and methods to be implemented for detecting image forgeries.

IV. REFERENCES:

[1] Jason Bunk, jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Natraj, Arjunafienner,

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