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According to latest survey of world health organization 77 million people face problem of unsafe water in allover the India. Despite the advancement in global technology, still there are not sufficient tools to measure the quality of water. Keeping in mind on this major issue we have proposed a model of water pollution detection system which is based on internet of things as well as it is cost efficient too. This model has capacity to measure and detect the pollution level as well as turbidity, temperature and many parameters. Turbidity measures the large no of suspended particles in water which is fully invisible and can’t be able to see with naked eyes. If water contains higher number of turbidity then it can cause various diseases like diarrhea, and cholera, while if turbidity is in low amount the water will consider pure and drinkable. PH is another important measure which measures the acidic and basic level of water. Temperature sensor measures the temperature of water that how much cold or hot it is.

Water quality monitoring, internet of things, rural development, machine learning, cloud computing, ESP8266, arduino micro-controller, RFID

[1] Anjana S, Sahana M N, Ankith S, K NAtarajan, K R Shobha “An IOT based 6LoWPAN enablesd Experient for Water Management”IEEE ANTS 2015 1570192963
[2] K. GOPAVANITHA “ALow Cost System for Real Time Water Quality Monitoring and Controlling using IoT”978-1-5386-1887-5/17/©2017IEEE
[3] Nikhil Kumar Koditala, “Water Quality Monitoring System using IoT and Machine Laarning,”978-1-5386-2599-6/18/ ©2018IEEE
[4] N Vijayakumar“A Real Time Monitoring of Water Quality IN IoT Evironment”978-1-4799-6818-3/15/©2015IEEE
[5] Ricardo Yauri, Milton Rios, Jinmi Lezama“Water Quality Monitoring of Peruvian Amazon Based in the Internet of Things”978-1-5090-6363-5/17/©2017IEEE
[6] R.Karthik Kumar, M.Chandra Mohan, S.Vengateshapandiyan, M.Mathan Kumar, R.Eswaran. ”Solar based advanced water quality monitoring system using wireless sensor network” 2014, International Journal of Science, Engineering and Technology Research, 2014
[7] S. Zhuiykov, “Solid-state sensors monitoring parameters of water quality for the next generation of wireless sensor networks,” Sens. Actuators B, Chem., vol. 161, no. 1, pp. 1–20, 2012
[8] T. P. Lambrou, C. G. Panayiotou, and C. C. Anastasiou, “A low-cost system for real time monitoring and assessment of potable water quality at consumer sites,” in Proc. IEEE Sensors, Oct. 2012, pp. 1–4.
[9] Uferah Shafi, Rafia Mumtaz, Hirra Anwar, Ali Mustafa Qamar “Surafce Water Pollution Detection Using Inter of Things”978-1-5386-8354-5/18/©2018IEEE
[10] Vaishnavi V. Daigavane, Dr. M.A Gaikwad. “Water Quality Monitoring System Based on IOT” 2017 Advances in Wireless and Mobile Com- munications, Nov 2017 ISSN 0973-6972
[11] http://circuitdigest.com/microcontroller projects/sending-email-using-arduino-and-esp8266-wi-fi-module
[12] http://circuitdigest.com/microcontroller-projects/arduino-humidity-measurement

A.Gupta, B.Gupta, K.K. Gola, "Internet of Things (IoT) for Detecting, Monitoring and Measuring Water Quality," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1114-1119, 2019.

There has been an increasing interest in the radiation immunity of circuits in recent years as modern integrated circuits operating in high radiation environment require careful attention to the soft errors resulting in bit upsets. These events are referred to as single-event upsets (SEUs). SEUs will become more severe as a result of technology scaling and play a pivotal role in memory system stability. Memory systems with lower sensitivity to SEUs offer better stability and reliability if ignored lead to catastrophic situations in fields such as medicine, aerospace, etc. Therefore, it has become crucial that the design of the memory arrays is SEU resilient. The proposed design achieves high soft-error tolerance for robust low power operation in high-radiation environments making use of the MTCMOS technique. Less leakage power consumption is an important reason why this technology is incorporated into larger systems such as memories. This, in turn, leads to improved efficacy along with reduced susceptibility to single-event upsets and lower power consumption.

Single Event Upset (SEU), rad-hardening (radiation hardening), Static Random Access Memory (SRAM), low power, Multi-threshold CMOS (MTCMOS)

[1] R. C. Baumann, “Radiation-induced soft errors in advanced semiconductor technologies,” IEEE Trans. Device Mater. Rel., vol. 5, no. 3, pp. 305–316, Sep 2005.
[2] J. L. Barth, C. Dyer, and E. Stassinopoulos, “Space, atmospheric, and terrestrial radiation environments,” IEEE Transactions on Nuclear Science, vol. 50, no. 3, pp. 466–482, 2003.
[3] Todd. R. Weatherford “Radiation effects in high speed III-V integrated circuits,” International Journal of High Speed Electronics and Systems Vol. 13, No.1 (2003) 277-292.
[4] P. E. Dodd and F. W. Sexton, “Critical charge concepts for CMOS SRAMs,” IEEE Trans. Nucl. Sci., vol. 42, no. 6, pp. 1764–1771, Dec. 1995.
[5] E.G. Stassinopoulos “The space radiation environment for electronics,” Proceedings of the IEEE Vol: 76, Issue: 11, Nov 1988.
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[7] P. E. Dodd and L. W. Massengill, “Basic mechanisms and modeling of single-event upset in digital microelectronics,” IEEE Transactions on Nuclear Science, vol. 50, no. 3, pp. 583–602, 2003.
[8] Alexander Fish, LiorAtias, Adam Teman “ A Low-Voltage Radiation-Hardened 13T SRAM Bitcell for Ultralow Power Space Applications ” IEEE Transactions on Very Large Scale Integration (VLSI) Systems (Volume: 24, Issue: 8, Aug. 2016).
[9] Mohab Anis, Mohamed Elmasry “Multi-Threshold CMOS Digital Circuits: Managing Leakage Power,” 2003.
[10] S. Mutoh, T. Douseki, Y. Matsuya, T. Aoki, S. Shigematsu, J. Yamada “1­V power supply high­speed digital circuit technology with multithreshold­voltage CMOS,” Issue 8, Volume 30, August 1995.
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[12] Rashmi Deshmukh, Anagha Zade, Rasika Bhusari “Temperature Monitoring and Regulating System for Power Saving”, International Journal of Computer Sciences and Engineering, Vol.1, Issue.2, pp.36-37, 2013.
[13] Anil Pratap “Analysis of Full and Half Adder Using Different Logic Style”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.6-9, Feb 2016.
[14] Lior Atias, Adam Teman, and Alexander Fish, “Single Event Upset Mitigation in Low Power SRAM Design,” IEEE 28th Convention of Electrical and Electronics Engineers in Israel, 2014.

Anusha Gandla, "A Low Power SEU Resilient 13T SRAM using MTCMOS," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1120-1125, 2019.

This research paper aims to present the study and implementation of artificial intelligence dietician which can simulate the experience of a human dietician. The main aim is to recommend to the users a perfectly planned diet according to their body parameters and their day to day activities using artificial intelligence. The online artificial dietician is a bot with artificial intelligence about human nourishments. It acts as a diet specialist similar to an actual dietician. We have also taken under consideration the health status of the user. We have used artificial intelligence as the driving technology. To select the diet of user it has to check various parameters and there can be various food items that pass the criteria. So to select the best among all, we take the help of Genetic Algorithm. Genetic Algorithm is our key algorithm, besides the Naïve Bayes algorithm. Genetic algorithm keeps on finding the best option from the pool of options while Naïve Bayes is used for the purpose of classification.

Genetic algorithm, Naïve Bayes Classifier, Artificial Intelligence

[1] G.Agapito, M.Simeoni, “DIETOS: a recommender system for adaptive diet monitoring and personalized food suggestion”, In the Proceedings of the 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), New York, NY, USA, pp.1-8, 2016.

[2] C.Snae, M.Bruckner, “FOODS: A Food-Oriented Ontology-Driven

System”, In the Proceedings of the 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, Phitsanulok, Thailand, pp.168-176, 2008.
[3] F.Wang, Y.Yuan, Y.Pan, B.Hu, “Study on the Principles of the Intelligent Diet Arrangement System Based on Multi-Agent”, In

the Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application, Shanghai, China, pp.264-268, 2008.

[4] H.Jen-Hsiao, C.Henry, “SmartDiet: A personal diet consultant for healthy meal planning”, In the Proceedings of the 2010 IEEE 23rd


International Symposium on Computer-Based Medical Systems (CBMS), Perth, WA, Australia, pp.421-425, 2010.

[5] H.Pruthi. H.Parvadiya, V.Rawool, J.Philip, “Artificial Intelligence Dietician” INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH Vol.31, Issue.2, pp.176-178, 2017.

[6] J.Barnett. M.Harricharan, D.Fletcher, “myPace: An Integrative Health Platform for Supporting Weight Loss and Maintenance

Behaviors” IEEE Journal of Biomedical and Health Informatics Vol.19, Issue.1, pp.109-116, 2015.
[7] G.Saranya, G.Geetha, M.Safa, “E-Antenatal assistance care using decision tree analytics and cluster analytics based supervised machine learning”, In the Proceedings of the 2017 International Conference on IoT and Application (ICIOT), Nagapattinam, India, pp.1-3, 2017.

[8] J.H.Kim, J.S.Park, Y.H.Lee, K.W.Rim, “Design of Diet Recommendation System for Healthcare Service Based on User

Information”, In the Proceedings of the 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, Seoul, South Korea, pp.516-518, 2009.
[9] Y.Lv, D.Li, “Improved Quantum Genetic Algorithm and Its Application in Nutritional Diet Optimization” In the Proceedings of the 2008 Fourth International Conference on Natural Computation, Jinan, China, pp. 460-464, 2008.

[10] Z.Pei, Z.Liu, “Nutritional Diet Decision Using Multi-objective

Difference Evolutionary Algorithm” In the Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, Wuhan, China, pp.77-80, 2009.

[11] S. Dubey, R. Jhaggar, R. Verma, D. Gaur, “Encryption and Decryption of Data by Genetic Algorithm” International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.42-46, 2017.

[12] S.Sharma, S.Khan, “Analysis of Cloud Security, Performance, Scalability and Availability (SPSA)” International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.1, 2019.

D.S. Zingade, Umar Shaikh, Shreyas Saisekhar, Umang Koul, Keshav Vaswani, "An Online Diet Recommendation System Based On Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1126-1130, 2019.

In recent times Virtualization and Cloud computing are two popular research directions. In contrast with the past, Virtualization is being used by a growing number of organizations for Server Consolidation, Dynamic Load Balancing, Testing and Development, Disaster Recovery, Improved System Reliability and Security and to reduce power consumption, and also provides high availability for critical applications, and streamlines application deployment and migrations. Information Technology resources can be delivered as services over the Internet to the end user through cloud computing. One of such important core technologies of cloud computing is Virtualization. In this paper, we present a detailed review on virtualization. Furthermore, we also discussed the Role of Virtualization in cloud computing, we also discussed the three main types of Virtualization technologies.

Cloud computing, Network, Virtualization, Technology, Memory

[1] Aaqib Rashid, Amit Kumar “Cloud Computing Characteristics and Services: A Brief Review”, International Journal of Computer Sciences and Engineering Vol.7(2), Feb 2019, E-ISSN: 2347-2693
[2] NIST, http://www.nist.gov/itl/cloud/index.cfm
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[5] Michael Kretzschmar, S Hanigk, “Security management interoperability challenges for collaborative clouds”, Systems and Virtualization Management (SVM), 2010, Proceedings of the 4th International DMTF Academic Alliance Workshop on Systems and Virtualization Management: Standards and the Cloud, pp. 43-49, October 25-29, 2010. ISBN:978-1-4244-9181-0,DOI: 10.1109/SVM.2010.5674744.
[6] B. Loganayagi, S. Sujatha, ―Creating virtual platform for cloud computing‖, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC 2010), 28-29 Dec. 2010, pp.1-4.
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[8] Karen Scarfone, Murugiah Souppaya, and Paul Hoffman, ―Guide to Security for Full Virtualization Technologies‖, Special Publication 800-125, National Institute of Standards and Technology (NIST), 2011.
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[17] Asma ben letaifa, Amed haji, Maha Jebalia, Sami Tabbane, ―State of the Art and Research Challenges of new services architecture technologies: Virtualization, SOA and Cloud Computing‖, International Journal of Grid and Distributed Computing 3(4), December 2010, 69-88.
[18] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield, ―Xen and the art of virtualization‖, In SOSP ’03: Proceedings of the nineteenth ACM symposium on operating systems principles (New York, NY, USA, 2003), ACM Press, pp. 164–177.
[19] IBM Virtual Infrastructure Access Service Product. https://www935.ibm.com/services/au/gts/pdf/end03005usen.pdf.
[20] B. Siddhisena, Lakmal Wruasawithana, Mithila Mendis, ―Next generation muti tenant virtualization cloud computing platform‖, In: Proceedings of 13th International conference on advanced communication technology(ICACT), vol. 12, no.3; 2011. p.405–10.

Aaqib Rashid, Amit Chaturvedi, "Virtualization and its Role in Cloud Computing Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1131-1136, 2019.

Stock index prediction is a continually evolving field. Prediction is used for technical and fundamental analysis by both short term traders and long terms investors. There are a multitude of theories and methodologies that exist in the area of stock index prediction. These range from older time-series econometric models to newer Artificial Neural Network (ANN) models. The scope and applicability of ANN is widening rapidly with newer and powerful architectures being proposed in the past few years. Significant amount of work has been done using ANN for stock index prediction, but most of it is done on a class of network architecture called Multi Layered Perceptron (MLP). In this work, we make use of two relatively newer ANN architectures – Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) for stock index prediction. We find that these two models offer better forecast accuracy compared to the MLP model for the chosen stock index.

Stock index prediction, MLP, LSTM, CNN

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[2] Majumder, M., and Hussian, M.D.A.(2009). “Forecasting Of Indian Stock Market Index Using Artificial Neural Network”, National Stock Exchange of India Limited
[3] Naeini, M.P., Taremian, H., and Hashemi, H.B. (2010). “Stock Market Value Prediction Using Neural Network”, CISIM, pp: 132 - 136
[4] Neenwi, S., Asagba, P.O., Kabari, L.G. (2013). “Predicting the Nigerian Stock Market Using Artificial Neural Network”, European Journal of Computer Science and Information, Vol I , No.1, pp.30-39
[5] Kar, A. (2013). “Stock Prediction using Artificial Neural Network”, Dept. of Computer Science and Engineering, IIT Kanpur
[6] Wanjawa, B.W., and Muchemi, L (2014). “ANN Model to Predict Stock Prices at Stock Exchange Markets”, Xiv:1502.06434

Navin S. Patel, Y.T. Krishne Gowda, "New Approaches for Stock Index Prediction Using Artifical Neural Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1137-1141, 2019.

Regular measurement and maintenance of calorie count of body is very essential and important for healthy living. Calorie count of body changes with change occurring in weight and height measurement. High body calories is a way to many diseases and disorders. Proper calorie count and nutritional value can be maintained by intake of healthy food. Keeping the knowledge of calories and nutritional value of each food item is a difficult task. Use of the emerging and rapidly growing smart phone technology for health maintenance can proved to be a great combination with outstanding results. The paper describes the system developed for food identification and calorie recognition, which also shows whether the given fruits and vegetables are fresh or not. Users can use the system with the help of developed android application on their smart phones. The system is trained and tested on the set of different food images to calculate its efficiency and accuracy. The results obtained proved that the system is accurate in recognizing the food item and showing its calorie contents. Also, the android application set the BMI value and depending on that give the daily calorie limit, which can be maintained by consuming the healthy food.

Calorie Count, Segmentation, Feature Extraction, Classification, Health Monitoring

[1] Haotian Jiang, Jammes Starkman, Menghan Liu, and Ming-Chun Huang “Food Nutrition Visualization on Google Glass”, IEEE Consumer Electronics Magazine, pp. 21-31, May 2018.
[2] Ridho Rahman Hariadi, Wijayanti Nurul Khotimah, Eko Adhi Wiyono, “Design and Implementation of Food Nutrition Information System using SURF and FatSecret API”, pp. 181-183.
[3] Arnel B. Ocay, Jane M. Fernandez, Thelma D. Palaoag, “NutriTrack: Android-based Food Recognition App for Nutrition Awareness”, 3rd IEEE International Conference on Computer and Communications, pp.2099-2104, 2017.
[4] S. Jasmine Minija, W.R. Sam Emmanuel, “Food image Classification using Sphere Shaped-Support Vector Machine”, Proceedings of the International Conference on Inventive Computing and Informatics (ICICI 2017) IEEE Xplore Compliant - Part Number: CFP17L34-ART, ISBN: 978-1-5386-4031-9, pp. 109-113.
[5] Diptee Kumbhar, Prof. Sarita Patil, “Mobile Cloud based System Recognizing Nutrition and Freshness of Food Image”, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017), pp. 709-714.
[6] Dr. Anne Moorhead, Dr Raymond Bond, Dr Huri Zheng, “Smart Food: Crowdsourcing of experts in nutrition and non-experts in identifying calories of meals using smartphone as a potential tool contributing to obesity prevention and management”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1777-1779, 2015.
[7] Agapito G., Calabrese B., Guzzi P. H., Cannataro M., Simeoni M., Car´e I., Lamprinoudi T., Fuiano G., Pujia A., “DIETOS: a recommender system for adaptive diet monitoring and personalized food Suggestion”, Fourth International IEEE Workshop on e-Health Pervasive Wireless Applications and Services, 2016.
[8] Haik Kalantarian, Nabil Alshurafa, Majid Sarrafzadeh, “A Wearable Nutrition Monitoring System”, 11th International Conference on Wearable and Implantable Body Sensor Networks, pp.75-80, 2014.
[9] Yoshiyuki kawano, Keiji yanai, “Real-time Mobile Food Recognition System”, IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2013.
[10] Lili Pan, Samira Pouyanfar, Hao Chen, Jiaohua Qin, Shu-Ching Chen, “DeepFood: Automatic Multi-Class Classification ofFood Ingredients Using Deep Learning”, IEEE 3rd International Conference on Collaboration and Internet Computing, pp.181-189, 2017.
[11] Muhammad Farooq, Edward Sazonov, “Feature Extraction Using Deep Learning for Food Type Recognition”, Springer International Publishing AG, I. Rojas and F. Ortuño (Eds.): IWBBIO, Part I, pp.464–472, 2017.

Rutuja Rewane, P. M. Chouragade, "Personalized Android Application for Food Identification and Calorie Count Visualization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1142-1147, 2019.

This paper focuses on the use of visual cryptography for the unique identification of user where different schemes like visual secret sharing schemes, halftone schemes, etc are used. The scheme uses users identifying data to be split into meaningful and meaningless shares. These shares can further be used by merging two shares and reveal the original data content. For additional security of data content, we can use different types of encryption techniques to hide the data content and the same can be retrieved by using the decryption method. The user is identified by the data content hidden in the two shares. One share of the data content can be put on the server side and the second share is at users end. At the time evaluation, one share is shown to the user at the server side and can be matched by the user with given second share of the data content.

Visual secret sharing (VSS), Visual cryptography (VC), Random grid (RG), Visual cryptography visual secret sharing (VCVSS), Random grid Visual secret sharing (RGVSS)

[1] Y.C. Hou, S.C. Wei, and C.Y. Lin, “Random- Grid based Visual Cryptography Schemes”, IEEE Transactions on Circuits and Systems for Video Technology, VOL. 24, NO. 5, May 2014.
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Anmol S. Budhewar, Shubhanand S. Hatkar, "Visual Cryptography Identity Specification Scheme," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1148-1152, 2019.

IOT is the induced structure foundation of openness, transportation and advancement. IOT sharp contraptions can understand the work environments of remote thriving viewing and additionally crisis see structure. It has clear utilization of sharp therapeutic organizations framework. In the human organizations structure the featured techniques and systems that assistance to the masters and investigators and specialists who make stunning contraption which is the up-degree to the present improvement. Undoubtedly, even information mining strategies and ML tally expect a basic work around there. The specialists vivifying their experts work to make programming with the assistance of AI check which can help aces with taking choice concerning both gauge and diagnosing of coronary sickness. The genuine objective of this examination paper is envisioning the coronary disease of a patient using ML estimations and web of web of things.

Blood pressure sensor, ESP8266 Wi-Fi module, Health monitoring systems, Heartbeat sensor, Internet of Things(IoT), Machine learning, Smart devices, Temperature sensor

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Parth Patel, Harshil Shah, Hiren V. Mer, "E-Health Diagnosis System using IoT and Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1153-1162, 2019.

This paper explores the application of the random forest algorithm for the alternate medicine recommendation. In this work the users able to search alternate medicine for their particular prescribed medicine. The main aim of the proposed system is to provide the users with alternate recommendation of medicines based on their content and shows the medicines as per ascending order of cost. It also has a facility to users to provide ratings for a particular medicine. Proposed System Architecture for content-based systems, one needs to assess the similarity of any many distinct medicines. If there exists a sufficient textual description for each medicine, this can be achieved via random forest algorithm. However, a user`s input medicine must contain all contain of medicine otherwise no similarity measuring can be performed.

Cost and Content Based Recommendation, Random Forest, Healthcare, Alternate Medicine

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Ankita D. Rewade, Sudhir W. Mohod, Sharad P. Bargat, "Content Based Alternate Medicine Recommendation By Using Random Forest Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1163-1168, 2019.

Big data has become an essential technology as many publicand private organizations have continuously collecteda vast amount of information regarding medical informatics, marketing, cyber security, fraud detection, and national intelligence. Deep learning is one of the remarkable machine learning techniques to find abstract patterns in Big data. Deep learning has achieved great success in variousbig data applications such as speech recognition, text understanding, and image analysis. In the field of data science, big data analytics and deep learning have become two highly focused research areas. Deep learning algorithm learns the multi-level representations and features of data in hierarchical structures through supervised and unsupervised strategies for the classification and pattern recognition tasks. In the last decade, deep learning has played a crucial role in providing the solutions for big data analytic problems. This paper provides a comprehensive survey of deep learning in Big data with the comparison of conventional deep learning methods, research challenges, and countermeasures. It also presents the deep learning methods, comparison of deep learning architectures, and deep learning approaches. Furthermore, this survey discusses the application-focused deep learning works in Big data. Finally, this work points out the challenges in big data deep learning and provide several future directions.

Big Data, Deep learning, Big data analytics, Machine learning, Deep learning architectures, and Challenges

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S.Umamageswari, M. Kannan, "A Survey of Essential Methods in Deep Learning for Big Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1169-1180, 2019.