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This paper offers a complete idea and information about carrier sense multiple access with collision‐avoidance (CSMA/CA) protocol. Firstly, what is the carrier sense multiple access protocol? and what is his job? Secondly, what is the collision‐avoidance technique? and What is the effect of adding this technique to such protocol? Thirdly, knowing how to model an exact analysis to describe the protocol with it is techniques and the performance of the multiple random access method with p‐persistent carrier sense multiple access and with collision‐avoidance (CSMA/CA) protocol for high‐speed and realizing fully‐distributed (D2D) device to device communication based on IEEE 802.15.4. The collision‐avoidance portion of CSMA/CA in this model is performed with a random pulse transmission procedure, in which a user with a packet ready to transmit initially sends some pulse signals with random intervals within a collision‐avoidance period before transmitting the packet to verify a clear channel. The system model consists of a finite number of users to efficiently share a common channel. The time axis is slotted, and a time frame has a large number of slots and includes two parts: the collision‐avoidance period and the packet‐transmission period. A discrete‐time Markov process is used to model the system operation. Also, it will be described by it is transmission state.

CSMA/CA, P-Persistent, D2D (device to device), IEEE 802.15.4

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Hazem Noori Abdulrazzak, Aya Ayad Hussein, "Performance Analysis of CSMA/CA Based on D2D Communication," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.652-657, 2019.

Text mining is a Computer Science technique to analyze text data. Text mining is text analysis, is the process of deriving high quality information from text. Text mining is to convert text into data for suitable analysis. It allows us to investigate relationship among patterns which would otherwise be extremely difficult. Various techniques are used to mining the frequent patterns in the given text which are applicable to analyze the information in huge documents. The parallel construction of FP-Trees and parallel mining on multi cores is a popular tree projection based mining algorithm. Once each processor counts the frequency of each item using its local data partition, all worker processors send the local count to the master processor which combines them and generate global count. The parallel implementation of FP-tree may show good speedups but sending the local results to master on distributed environment and merging the patterns count on master core are overhead which consumes a considerable time. This study aims at to analyze various frequent pattern mining techniques used to extract information from texts especially on multi cores and going to adopt a new technique for finding frequent patterns, which used the Dictionary based compression algorithm(LZW). The new technique is implemented with single processor as so as with multi processor using message passing technique. The main objective of this research is enhancing the speed and reduce the memory consumption required to extract the frequent patterns form the given textual data. The parallel implementation of our proposed LZW based algorithm with three datasets Webdoc, Kosarak and Trump is compared with parallel implementation of FP-Growth on single and multi core. The results shows good performance in speedup, Latency and Efficiency in proposed LZW based algorithm.

Parallel FP-Growth, Frequent Keywords Mining, Multi core Systems

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M. Deeba, Mary Immaculate Sheela, "Text Mining Using Frequent Pattern Analysis and Message Passing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.658-667, 2019.

Startup companies are newly born companies or entrepreneurial ventures which are predominantly based on brilliant idea, innovation and statistical study. One of the preeminent obstacle of Startup’s is to seek capital and Human Intelligence to solve complex tasks of the product and in return bolster their project growth which can be achieved by ‘Crowdfunding’ and ‘Crowdsourcing’. Crowdfunding is a platform offering entrepreneurs and project owners the possibility to raise money from an undefined group of online users ie ‘Crowd’ while Crowdsourcing involves obtaining work,human intelligence or opinions from a large group of people which submit their data via the Internet. In this paper, we conceptualize a blockchain-based system “CrowdSF” for crowdsourcing and crowdfunding. In which we will try to integrate both platform together where idea creator can recruit the worker to execute the project or seek masses for funding their project at one single platform with security.

Blockchain, Collective intelligence, Crowdfunding, Crowdsourcing, Startup

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[2 ]Giones, F. & Oo, P., 2017. “How Crowdsourcing and Crowdfunding are redefining innovation management”. In A. Brem & E. Viardot, eds. Revolution of Innovation Management. London:Palgrave Macmillan UK. http://link.springer.com/chapter/10.1057/978-1-137-57475-6_3
[3]Jackie Zimmermann , “Rewards-Based Crowdfunding: What It Is, When It Works” ,Updated Dec. 6, 2017.
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[4]Ordanini, A.; Miceli, L.; Pizzetti, M.; Parasuraman, A. (2011). "Crowd-funding: Transforming customers into investors through innovative service platforms". Journal of Service Management. 22 (4): 443. doi:10.1108/09564231111155079. (also available as Scribd document)
[5]Ajay Agrawal,Christian Catalini, Avi Goldfarb , “Some simple economics of Crowdfunding”,working paper 19133,June 2013
JEL No. D47,D82,G21,G24,L26,L86,R12,Z11 http://www.nber.org/papers/w19133.pdf
[6] Mary Thibodeau, Operations Manager at TruDex.io (2017-present) “How can blockchain be used in crowdsourcing?”https://www.quora.com/How-can-blockchain-be-used-in-crowdsourcing
[7]oon seok Lee, Mingxuan Sun, Guy Lebanon, “A Comparative Study of Collaborative Filtering Algorithms”https://arxiv.org/pdf/1205.3193.pdf , arXiv:1205.3193v1 [CS.IR} 14th may 2012
[8] S. Debnath, N. Ganguly, and P. Mitra. Feature weighting in content based recommendation system using social network analysis. In Proceedings of the 17th international conference on World Wide Web, pages 1041–1042, 2008.
[9] Schenk, Eric; Guittard, Claude (January 1, 2009). "Crowdsourcing What can be Outsourced to the Crowd and Why". Retrieved October 1, 2018.
[10]"Definition of Crowdfunding". Retrieved 2018-12-3.https://www.ukcfa.org.uk/what-is-crowdfunding/
[11]“Waze - GPS, Maps, Traffic Alerts & Live Navigation” https://www.apkmirror.com/apk/waze/waze-gps-maps-traffic-alerts-live-navigation/

Er. Waheeda Dhokley, Saurabh Gupta, Ganesh Pawar, Abrar Shaikh, "Crowdsourcing and Crowdfunding Platform using Blockchain and Collective Intelligence," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.668-673, 2019.

Emotion recognition is an approach to detect and analyze different human emotions from their facial expressions. We, humans, have versatile and consistent abilities through variable among individuals in detecting emotions, but the high-speed computer/ machines lag humans in this capability. Here, an approach is developed which will make the machines detect human emotions and learn to behave with them by analyzing their emotions. In this paper, computer vision techniques are used for feature extraction and machine learning tools and Artificial Intelligence tools for training purposes. This paper proposes a neural network based solution combined with image processing in classifying the universal emotions – Happiness, Sadness, Anger, Disgust, Surprise, and Fear.

Emotions, Neural Networks, Artificial Intelligence, Data Mining, Image Processing

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[4] Ekman, P. and Oster, H., 1979. Facial expressions of emotion. Annual review of psychology, 30(1), pp.527-554.
[5] Ekman, P. and Rosenberg, E.L. eds., 1997. What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). Oxford University Press, USA.
[6] Sayette, M.A., Cohn, J.F., Wertz, J.M., Perrott, M.A. and Parrott, D.J., 2001. A psychometric evaluation of the facial action coding system for assessing spontaneous expression. Journal of Nonverbal Behavior, 25(3), pp.167-185.
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[8] Gupta A, Sharma E, Sachan N, Tiwari N. Door Lock System through Face Recognition Using MATLAB. International Journal of Scientific Research in Computer Science and Engineering. 2013 Jun;1(3).

Saniya Zahoor , "Recognizing Human Emotional State from a Machine," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.674-678, 2019.

The sharing data issues with the high-quality airline props on various media outlets. The new supplier can try to collect information about the information provided by various suppliers. Revised dates are constantly increasing for conflict resolution data that keeps data specific. The plan provided community information based on the issue of unpublished collections of high-quality data from different data providers. Therefore, security, which ensures that non-media outlets are carrying out the security measures given to gather new producers. There will be customers M per customer to avoid purchasing an anonymous definition for each customer. To overcome this, there is a new system called coverage that offers better information on the use of all kinds of foods needed for high-level information.

Data anonymization, Overlapping slicing, Data Privacy, Security, Integrity

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Ashok Koujalagi, "A Survey on Novel Sequestration Adroitness to Secure Susceptible Micro Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.679-683, 2019.

Relational Database Management System (RDBMS) has been the power house of database industry since decades. However with the evolution of big data, they are loosing their importance due to many reasons. With the rapid growth in internet and social network, graph databases are seen as an promising alternative to relational database for determining relationships optimally and quickly. These databases can be of great help to the companies struggling with traditional databases and want some new database that can replace these legacy databases. This paper presents a theoretical investigation and comparison of the relative usefulness between relational database (MySQL) and the graph database (Neo4j).

Big Data, Graph Database, Relational Database

[1] N. Leavitt, “Will NoSQL Databases Live Up to Their Promise?”, IEEE Transactions on Computer, ISSN: 0018-9162, Vol. 43, No. 2, pp. 12-14, 2010.
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[7] C Yaowen, “Comparison of graph databases and relational databases when handling large scale social data”, Master’s Thesis, College of Graduate Studies and Research, Department of Computer Science University of Saskatchewan, Saskatoon, September 2016.
[8] S. Medhi, H. K. Baruah, “Relational Database and Graph Database: A Comparative Analysis”, Journal of Process Management – New Technologies International, Vol. 5, No 2, 2017.
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[10] S. S. Marudkar, H. R.Vyawahare, “Performance Analysis of Relational and Graph Database”, International Research Journal of Engineering and Technology, Vol. 5 Iss.4, pp 4686-4692, 2018
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[15] J. Partner, A. Vukotic, N. Watt, “Neo4j in Action”, 1st ed. Mannig, 2014.

H.R. Vyawahare1*, P.P. Karde2, V.M. Thakare3 , "Graph Database: An Alternative for Relational Database," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.685-691, 2019.

Temporary keyword search seek on secret information in a cloud condition is the principle focal point of this exploration. The cloud suppliers are not completely trusted. In this way, it is important to redistribute information in the encoded frame. In the attribute based keyword search (ABKS) plans, the approved clients can produce some hunt tokens and send them to the cloud for running the pursuit task. These hunt tokens can be utilized to remove all the ciphertexts which are created whenever and contain the comparing temporary keyword search. Since this may prompt some data spillage, it is increasingly secure to propose a plan in which the hunt tokens can just concentrate the ciphertexts produced in a predefined time interim. To this end, in this paper, we present another cryptographic crude called key policy -attribute based temporary keyword search(KPABTKS) which give this property. To assess the security of our conspire, we formally demonstrate that our proposed plan accomplishes the watchword mystery property and is secure against specifically picked watchword assault (SCKA) both in the arbitrary prophet demonstrate and under the hardness of Decisional Bilinear Diffie-Hellman (DBDH) suspicion. Moreover, we demonstrate that the intricacy of the encryption calculation is direct concerning the quantity of the included qualities. Execution assessment demonstrates our plan`s reasonableness

Searchable encryption, attribute-based encryption, Temporary keyword search, cloud security

[1] Q. Zheng, S. Xu, and G. Ateniese, “Vabks: Verifiable attribute-based keyword search over outsourced encrypted data,” in INFOCOM, 2014 Proceedings IEEE. IEEE, 2014, pp. 522–530.
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[4] Y. Yu, J. Ni, H. Yang, Y. Mu, and W. Susilo, “Efficient public key encryption with revocable keyword search,” Security and Communication Networks, vol. 7, no. 2, pp. 466–472, 2014.
[5] E.-J. Goh et al., “Secure indexes.” IACR Cryptology ePrint Archive, vol. 2003, p. 216, 2003.
[6] Z. Fu, X. Wu, C. Guan, X. Sun, and K. Ren, “Toward efficient multikeyword fuzzy search over encrypted outsourced data with accuracy improvement,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 12, pp. 2706–2716, 2016.
[7] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy-preserving multikeyword ranked search over encrypted cloud data,” IEEE Transactions on parallel and distributed systems, vol. 25, no. 1, pp. 222–233, 2014.
[8] H. Li, Y. Yang, T. H. Luan, X. Liang, L. Zhou, and X. S. Shen, “Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data,” IEEE Transactions on Dependable and Secure Computing, vol. 13, no. 3, pp. 312–325, 2016.
[9] A. Awad, A. Matthews, Y. Qiao, and B. Lee, “Chaotic searchable encryption for mobile cloud storage,” IEEE Transactions on Cloud Computing, vol. PP, no. 99, pp. 1–1, 2017.
[10] J. Li, D. Lin, A. C. Squicciarini, J. Li, and C. Jia, “Towards privacypreserving storage and retrieval in multiple clouds,” IEEE Transactions on Cloud Computing, vol. 5, no. 3, pp. 499–509, July 2017.
[11] J. Li, R. Ma, and H. Guan, “Tees: An efficient search scheme over encrypted data on mobile cloud,” IEEE Transactions on Cloud Computing, vol. 5, no. 1, pp. 126–139, Jan 2017.
[12] J. Baek, R. Safavi-Naini, and W. Susilo, “Public key encryption with keyword search revisited,” in Computational Science and Its Applications– ICCSA 2008. Springer, 2008, pp. 1249–1259.
[13] H. Yin, Z. Qin, J. Zhang, L. Ou, and K. Li, “Achieving secure, universal, and fine-grained query results verification for secure search scheme over encrypted cloud data,” IEEE Transactions on Cloud Computing, vol. PP, no. 99, pp. 1–1, 2017.
[14] K. Liang and W. Susilo, “Searchable attribute-based mechanism with efficient data sharing for secure cloud storage,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 9, pp. 1981–1992, 2015.
[15] J. Han, W. Susilo, Y. Mu, and J. Yan, “Attribute-based oblivious access control,” The Computer Journal, vol. 55, no. 10, pp. 1202–1215, 2012.
[16] “Attribute-based data transfer with filtering scheme in cloud computing,” The Computer Journal, vol. 57, no. 4, pp. 579–591, 2014.
[17] Y. Shi, Q. Zheng, J. Liu, and Z. Han, “Directly revocable key-policy attribute-based encryption with verifiable ciphertext delegation,” Information Sciences, vol. 295, pp. 221–231, 2015.
[18] J. Bethencourt, A. Sahai, and B. Waters, “Ciphertext-policy attribute-based encryption,” in Security and Privacy, 2007. SP’07. IEEE Symposium on. IEEE, 2007, pp. 321–334.
[19] H. Deng, Q. Wu, B. Qin, J. Domingo-Ferrer, L. Zhang, J. Liu, and W. Shi, “Ciphertext-policy hierarchical attribute-based encryption with short ciphertexts,” Information Sciences, vol. 275, pp. 370–384, 2014.
[20] A. Balu and K. Kuppusamy, “An expressive and provably secure ciphertext-policy attribute-based encryption,” Information Sciences, vol. 276, pp. 354–362, 2014.
[21] J. Han, W. Susilo, Y. Mu, J. Zhou, and M. H. A. Au, “Improving privacy and security in decentralized ciphertext-policy attribute-based encryption,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 3, pp. 665–678, 2015.
[22] J. Han, W. Susilo, Y. Mu, and J. Yan, “Privacy-preserving decentralized key-policy attribute-based encryption,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 11, pp. 2150–2162, 2012.

C. Pradeepthi, V.J. Vijaya Geetha, "Extraction of Cipher Texts for Temporary Keywords Search on Confidential Data in the Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.692-696, 2019.

Accidental detection rescue system is an application intended for the use of providing assistance to the victim in case of emergency. The credibility and accessibility of this application is designed in accordance with the existing systems and improved with enhanced features and technology. With an application detecting the location of the accident and image processing classifying it as accidental or fake image the application is built to provide assistance to the end user in a systematic manner. The proposed system i.e. the Accident Detection and Rescue System (ADRS) consists of two phases; the detection phase which is used to detect type of accident based on the image captured and the notification phase, immediately after an accident has occurred, is used to send detailed information such as images, accident location, date and time of accident, etc to the emergency responder for fast recovery. ADRS will ensure that the victim of the accident is rescued within an hour of accident which is also known as Golden Hour. Malicious use of the application will be prohibited and it will be available to all users at any particular instance of time

Golden Hour Response, Medical Assistance, Real time location, Broadcasting, Geo-Fencing, Image Processing

[1] IJCSMC, Vol. 4, Issue. 4, April 2015, pg.620 – 635, “Car Accident Detection and Notification System Using Smartphone”, Hamid M. Ali, Zainab S. Alwan.
[2] IATSS , Vol 32, Issue 2, 2018, pg 58-67, “Identification of factors in road accidents through in-depth accident analysis”, Mouyid BIN ISLAM, Kunnawee Kanitpong.
[3] International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August-2013, “Improving Railway Safety with Obstacle Detection and Tracking System using GPS-GSM Model”, Nisha S.Punekar , Archana A. Raut.
[4] Special Issue Published in International Journal of Trend in Research and Development, ISSN: 2394-9333, “Embedded Based Train Accident Prevention System”, T.Sivaranjani, R.Dhivya, S.Indhumati, K.Ramesh Aravind and P.Sethuraman.
[5] IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT) e-ISSN: 2319-2402,p- ISSN: 2319-2399.Volume 10, Issue 10 Ver. I (Oct. 2016), PP 75-86, “GPS & GIS In Road Accident Mapping And Emergency Response Management”, Evangeline Muthoni Njeru, Andrew Imwati.
[6]Jun Okamoto Jr., Alexander Csóka Roque, Fabricio Schiavo, Bruno Massoni Sguerra, Bruno Alan Miyamoto, Filipe Assis Mourão, Thiago Yukio Alves, Andressa de Paula Suiti Escola Politécnica of the University of São Paulo, Brazil, “Addressing the Golden Hour: A machine learning approach to improve emergency response time”
[7]Location Detection using GPS: https://www.instructables.com/id/Track-your-location-without-using-GPS-using-LAC-a/
[8]Image Processing:https://medium.com/@qhutch/android-simple-and-fast-image-processing-with-renderscript-2fa8316273e1
[9]Nearby Resources: https://www.androidtutorialpoint.com/intermediate/google-maps-search-nearby-displaying-nearby-places-using-google-places-api-google-maps-api-v2/
[10]Golden Hour Response:https://www.thedailystar.net/opinion/golden-hour-the-lives-accident-victims-146517

Asadullah Shaikh, Zainab Gangerdiwala, Sarah Shaikh, Husain Kothari, "Accident Detection and Smart Rescue with Real Time Location using Image Processing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.697-700, 2019.

Technology has a significant contribution in the digital communication for visually impaired persons. There is a significant need transliteration of Braille Characters into regional language. The “Software for Transliteration of Braille Character to Gujarati Text using Python” has three key factors keep in mind. First there is digital communication using technology to provide alternative means of reading and writing. Next, technology can help in the production of digital content converted from Braille to Gujarati or vice versa. A third factor for technology is to provide improved access to information like import, export and saving the digital content. It is sometimes quite difficult to be certain whether a technology or system will meet particular needs or not. This paper is about developing a prototype using Python Programming Language, because Python is a suitable language for both learning and real world programming. Python is an extremely potent and high level object-oriented programming language. We have provided various techniques describe as a literature to develop the tool and also identified the challenges that might be faced at the time of recognition and conversion

Braille translation, Braille display terminal, Braille character conversion modules, Unicode recognition using Python, Braille cells, Braille to Gujarati

[1] Chitte PP, P. Y. (2015 Jan). “Braille to text and speech for cecity persons”. International Journal of Research in Engineering and Technology, Vol. 4(1):263–8.
[2] Acharya. (n.d.). Disabilities. Retrieved from International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639)
[3] Hardik Vyas, P. V. Virparia (December 2014). “Optical Gujarati Braille recognition: A Review”. International journal of emerging technologies and applications in engineering, technology and sciences (ij-eta-ets).
[4] R. A. J. Gildea, G. H. (March 1973). “Computerised Braille production. International Workshop in Munster (Germany). Munster, Germany”: Rechenzentrum der University.
[5] Padmavathi S, M. K. (2013 Jun). “Conversion of braille to text in English, Hindi and Tamil languages”. International Journal of Computer Science and Applications, 3(3):19–32.
[6] Wajid M, A. M. (2011). “Imprinted braille character pattern recognition using image processing techniques”. IEEE International Conference on Image Information processing. Shimla, India.
[7] Hardik Vyas, P. V. Virparia (July 2018). “Transliteration of Braille Character to Gujarati Text - The Model”. International Journal of Engineering Research & Technology (IJERT), V.07,I.07.
[8] Kuhlman, Dave. "A Python Book: Beginning Python, Advanced Python, and Python Exercises". 23 June 2012.
[9] TIOBE Software Index (2011). "TIOBE Programming Community Index Python"
[10] Hardik Vyas, P. V. Virparia (New Delhi, 2015). “Gujarati Braille Text Recognition: A Design Approach. Advances in Intelligent Systems and Computing” - Springer, 31-40.

H.A. Vyas, P.V. Virparia, "Transliteration of Braille Character to Gujarati Text – The Application," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.701-705, 2019.

The technological evolution in VLSI design plays important role in the field of microelectronics. The microelectronic device such as microcontroller is widely used in the data acquisition system (DAS). The DAS for the physics laboratory application can be developed with microcontroller like Advanced RISC Machine (ARM) and supporting hardware. This paper comprises detail explanation about the data acquisition system using ARM 7 LPC -2148 and C – language programs. The system includes peripherals such as ADC, DAC, keypad, character LCD and external interrupt. The software programs for interfacing them with microcontroller are written and prescribed here for the first time learner. The physics experimental parameters like temperature, pressure can be acquired by the developed DAS.

ARM, LPC2148, Microcontroller, Physics experiment, ADC, DAC

[1] S. J. Pérez, M. A. Calva , R. Castañeda, “A microcontroller-based data logging system.,” Journal of the Mexican Society of Instrumentation Instrumentation and Development, Vol. 3 No. 8, pp.24-30,1997.
[2] Shubhangi R. Saraf, Rajesh M. Holmukhe, “Microcontroller Based Data Acquisition System For Electrical Motor Vibrations using VB software”, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2 No. 5,pp.727-737, Oct-Nov 2011.
[3] Darko Hercog, Bojan Gergič, “A Flexible Microcontroller-Based Data Acquisition Device,” Sensors, Vol.14,pp. 9755-9775, 2014.
[4] Juca, S.C.S.; Carvalho, P.C.M.; Brito, F.T., “A Low Cost Concept for Data Acquisition Systems Applied to Decentralized Renewable Energy Plants,” Sensors, 11, pp.743–756, 2011.
[5] R Maad, G.A. Johansen, “Experimental analysis of high-speed gamma-ray tomography performance,” Meas. Sci. Technol. Vol.19, no. 8, pp. 085502, 2008.
[6] Priyang Bhatt, Bhasker Thaker, Neel Shah, “A survey on developing Secure IoT products,” International Journal of Scientific Research in Computer Science and Engineering. Vol.6, no. 4, pp.41-44, 2018
[7] Kester, Walt, ed., “The Data Conversion Handbook,”Elsevier:Newnes, ISBN 0-7506-7841-0.
[8] Shubhangi R.Saraf, Rajesh M.Holmukhe, “Microcontroller based data acquisition System for electrical motor vibrations using VB software, Indian Journal of Computer Science and Engineering. Vol. 2,no. 5, pp.727-737, 2011.

Pravin Bhadane, Suchita Bhangale, "ARM based Data Acquisition System for Physics Experiments," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.706-709, 2019.