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Nowadays due to busy and hectic lifestyle, many people cannot pay enough attention to their health. Stress, junk food, obesity, smoking and lack of exercise leads to heart diseases. It is one of major cause leading rise to death rate. ECG (Electrocardiogram) is the best and easiest way to record and analyze the electrical and muscular activities of heart. Due to nonlinearity and complexity of ECG signals, it requires significant amount of training to analyze and study the ECG waveform. For preventive measures and predictive analysis, it is necessary to analyze these waveforms in faster, efficient way and in real time too. Number of methods and techniques have been developed in recent time. Different techniques and methodologies are discussed in this literature review.

ECG Signal, Arrhythmia, Feature Extraction, Feature Selection

[1] Venkatesan, C., P. Karthigaikumar, Anand Paul, S. Satheeskumaran, and R. Kumar. ”ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications.” IEEE Access 6 (2018): 9767-9773
[2] Li, Qiao, Cadathur Rajagopalan, and Gari D. Clifford. ”Ventricular fibrillation and tachycardia classification using a machine learning approach.” IEEE Transactions on Biomedical Engineering 61, no. 6 (2014): 1607-1613
[3] Hammad, Mohamed, Asmaa Maher, Kuanquan Wang, Feng Jiang, and Moussa Amrani. ”Detection of abnormal heart conditions based on characteristics of ECG signals.” Measurement 125 (2018): 634-644
[4] Alonso-Atienza, Felipe, Eduardo Morgado, Lorena Fernandez-Martinez, Arcadi Garc´ıa-Alberola, and Jos´e Luis Rojo-Alvarez. ”Detection of life-threatening arrhythmias using feature selection and support vector machines.” IEEE Trans. Biomed. Eng 61, no. 3 (2014): 832-840
[5] Xia, Yufa, Huailing Zhang, Lin Xu, Zhifan Gao, Heye Zhang, Huafeng Liu, and Shuo Li. ”An automatic cardiac arrhythmia classification system with wearable electrocardiogram.” IEEE Access 6 (2018): 16529-16538
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[9] Zhang, Xu-Sheng, Yi-Sheng Zhu, Nitish V. Thakor, and Zhi-Zhong Wang. ”Detecting ventricular tachycardia and fibrillation by complexity measure.” IEEE Transactions on biomedical engineering 46, no. 5 (1999): 548-555
[10] Kuo, S. ”Computer detection of ventricular fibrillation.” Proc. of Computers in Cardiology, IEEE Comupter Society (1978): 347-349
[11] Barro, S., R. Ruiz, D. Cabello, and J. Mira. ”Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system.” Journal of biomedical engineering 11, no. 4 (1989): 320-328
[12] Amann, Anton, Robert Tratnig, and Karl Unterkofler. ”Detecting ventricular fibrillation by time-delay methods.” IEEE Transactions on Biomedical Engineering 54, no. 1 (2007): 174-177
[13] Jekova, Irena, and Vessela Krasteva. ”Real time detection of ventricular fibrillation and tachycardia.” Physiological measurement 25, no. 5 (2004): 1167
[14] Jekova, Irena. ”Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set.” Biomedical Signal Processing and Control 2, no. 1 (2007): 25-33
[15] Li, Qiao, Roger G. Mark, and Gari D. Clifford. ”Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter.” Physiological measurement 29, no. 1 (2007): 15
[16] Thakor, Nitish V., Y-S. Zhu, and K-Y. Pan. ”Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm.” IEEE Transactions on Biomedical Engineering 37, no. 9 (1990): 837-843
[17] Arafat, Muhammad Abdullah, Abdul Wadud Chowdhury, and Md Kamrul Hasan. ”A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram.” Signal, Image and Video Processing 5, no. 1 (2011): 1-10
[18] Amann, Anton, Robert Tratnig, and Karl Unterkofler. ”Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators.” Biomedical engineering online 4, no. 1 (2005): 60
[19] Anas, Emran M. Abu, Soo Y. Lee, and Md K. Hasan. ”Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions.” Biomedical engineering online 9, no. 1 (2010): 43
[20] Dzwonczyk, Roger, Charles G. Brown, and Howard A. Werman. ”The median frequency of the ECG during ventricular fibrillation: its use in an algorithm for estimating the duration of cardiac arrest.” IEEE Transactions on Biomedical Engineering 37, no. 6 (1990): 640-646
[21] Zhang, Xu-Sheng, Yi-Sheng Zhu, Nitish V. Thakor, and Zhi-Zhong Wang. ”Detecting ventricular tachycardia and fibrillation by complexity measure.” IEEE Transactions on biomedical engineering 46, no. 5 (1999): 548-555
[22] Amann, Anton, Robert Tratnig, and Karl Unterkofler. ”A new ventricular fibrillation detection algorithm for automated external defibrillators.” database 1, no. 2 (2005): 3
[23] Li, Haiyan, Wenguang Han, Chao Hu, and Max Q-H. Meng. ”Detecting ventricular fibrillation by fast algorithm of dynamic sample entropy.” In Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference on, pp. 1105-1110. IEEE, 2009

Bhagyashri Bhirud, Vinod Pachghare, "ECG Signal Feature Extraction and Classification: Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.1-6, 2019.

The last decade has witnessed a shift in manufacturing processes from basic automation and event control to smart systems that create virtual blueprint which is transformed into real-world product. This transformation, dubbed as Industry 4.0 is an optimization of the practices which took place previously in the industries. The driving force behind this revolution has been machine learning and Internet of Things (IoT). Internet of Things is a term for devices that have the capability to connect and collaborate amongst themselves to minimize human intervention. In order to further enhance this connectivity, machine learning techniques are being implemented, that enable decision making through data analysis. This paper aims to demonstrate the use of these techniques in the manufacturing industry in the context of Industry 4.0. It will also shed light upon some of the upcoming applications of the same, including intelligently forecasting the estimated demand, detecting the anomalies within the product, optimizing manufacturing processes and securing the entire framework. The future use cases of this technology will be addressed in this paper.

Machine Learning, Internet of Things, Industry 4.0, Manufacturing, Data Analytics

[1] S.K. Bose, B. Kar, M. Roy, P.K. Gopalakrishnan, A. Basu, "ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing." arXiv preprint arXiv: 1811.00873, 2018.
[2] M. Haselmann, D.P. Gruber, P. Tabatabai, "Anomaly Detection using Deep Learning based Image Completion." arXiv preprint arXiv:1811.06861, 2018.
[3] S. Han, T. Gong, M. Nixon, E. Rotvold, K.Y. Lam, K. Ramamritham. “RT-DAP: A Real-Time Data Analytics Platform for Large-scale Industrial Process Monitoring and Control.” arXiv preprint arXiv: 1802.07855, 2018.
[4] G. Li, X. Yang, D. Chen, A. Song, Y. Fang, J. Zhou, “Tool Breakage Detection using Deep Learning” arXiv:1808.05347, 2018.
[5] J. Canedo, A. Skjellum, “Using machine learning to secure IoT systems”, 14th Annual Conference on Privacy, Security and Trust (PST), 2016.
[6] R.B. Shetty, "Predictive Maintenance in the IoT Era." Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, 2018.
[7] A. Kanawaday, A. Sane, "Machine learning for predictive maintenance of industrial machines using IoT sensor data." Software Engineering and Service Science (ICSESS), 8th IEEE International Conference on. IEEE, 2017.
[8] S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Globally and locally consistent image completion,” ACM Transactions on Graphics, vol. 36, no. 4, pp. 1–14, 2017.
[9] J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, “Generative image inpainting with contextual attention.”, arXiv preprint, 2018.

R.R. Gore, J.D. Patil, "A Study of Machine Learning and IoT in Manufacturing industries", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.7-11, 2019.

Security management systems in colleges, flats and gated communities have forever been supportedarchaic pen and paper system. Security guards pay most of their time in holding the records. Entrizee is an intelligentSecurity Management for Gated Premises that automates and computerizes manual tasks at the main gate(s). This system will be designed to maximize the security by providing an app to assist in entering the details of the user, keeping the records and perform analytics on it, which would otherwise have to be performed manually. Remaining straightforward to grasp and use, the system will meet the user’s expectations of minimizing the time required at the gates for physically entering all the details. It would also help the gatekeepers to keep a track of the people who are coming or leaving the premises after a certain specified time duration and take actions on them accordingly.

Security, Blockchain, SHA-256, Apriori algorithm

[1] Sonam Khedkar1, Swapnil Thube2, “Real Time Databases for Applications”, International Research Journal of Engineering and Technology (IRJET),June -2017
[2] Navdeep Singh, “Study of Google Firebase API for Android”, International Journal of Innovative Research in Computer and Communication Engineering, September 2016
[3] Nivedan Bhardwaj, Ritesh Kumar, RupaliVerma, Alka Jindal and Amol P. Bhondekar, “Decoding Algorithm for color QR code:A Mobile Scanner Application”,2016 Fifth International Conference on recent trends in information technology
[4] Tien Tuan AnhDinh, Rui Liu , Meihui Zhang , Gang Chen, Beng Chin Ooi, Ji Wang , “Untangling Blockchain: A Data Processing View of Blockchain Systems”, IEEE transactions on Knowledge and Data Engineering
[5]FlorentChabaud and Antoine Joux.Differential,”Collisions in SHA-0.”, Springer1998
[6] Libing Wu, Kui Gong, Yanxiang He, XiaohuaGe, Jianqun Cui, “A Study of Improving Apriori Algorithm”, National Natural Science Foundation of China.
[7]Shilpa,SunitaParasher, “ Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data”,International Journal of computer applications, October 2011.
[8] Xiaomei Yu, Hong Wang, Xiangwei Zheng, “New Adaptions for classification algorithm for mining frequent itemsets from uncertain data”, IEEE
[9] Yongmei Liu and Yong Guan, “FP-Growth Algorithm for Application in Research of Market Basket Analysis”, IEEE 2008
[10] lugendraDongre, GendLalPrajapati,“The Role of Apriori Algorithm for Finding the Association Rules in Data Mining”,IEEE 2014
[11] HendraGunawan,Evizal Abdul Kadir,“Integration Protocol Student AcademicInformation to Campus RFID Gate Pass System”,IEEE 2017.
[12] HE Yaru,JIANGYingzi,“ The design and implementation of residential parking spaces management and information issuing system”,2013 Fourth International Conference on Digital Manufacturing & Automation
[13] Ryan Ercel O. Paderes, “A Comparative Review of Biometric Security Systems”, 2015 8th International Conference on Bio-Science and Bio-Technology.

Ashwini Jarali, Snehal Kodilkar, Shubam Tondare, Ganesh Kudale, Siddharth Patel, "Entrizee- A QR based Digital Gate Security Management System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.12-17, 2019.

With proliferation of the internet based media application; like SMS, video messaging, has resulted in a surge of data sharing. Applications like WhatsApp, Facebook, attracted large number of users because of easy chat conversations. WhatsApp is also used in small-scale industries for business purpose where user can write message is any language, in short form and understanding the context in chat is very important. There is no standard text which can be conventional machine understandable. Categorization can form personification; but there is lack of support to interpretation the content in the text messages with direct and indirect meaning. Map reduce framework can be used for transliteration process with frequent term visualization. Also messages can be with intuitive background where safety features are required to focus. Hence in this paper a survey for personification of embedded media data through the confluence of embedded media data analytic and machine learning techniques is proposed.

Machine Learning, Personification, Safety

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[3] Moses L. Gadebe and Okuthe P. Kogeda, “Personification of Bag.of.features Dataset for Real Time Activity Recognition”, IEEE, 2016.
[4] Francesco Musumeci, et.al.,"A Survey on Application of Machine Learning Techniques in Optical Networks", April 2018.
[5] Rizky Septiani, et.al.,"Factors that Affecting Behavioral Intention in Online Transportation Service",Elsevier, Vol. 124, pp. 504-512, 2017.
[6] 18Stefanos Doltsinis, et.al.,"A Symbiotic Human ML Approach for Production Ramp-up",IEEE Trans. Vol. 48, No. 3, pp. 229-240, 2017.
[7] Aravind Kota Gopalakrishna et.al.,“Evaluating ML algorithms for applications with humans in the loop” IEEE 14th International Conf. on Networking, Sensing and Control (ICNSC) 2017.
[8] Namit Juneja, Rajesh Kumar,"Generating Analytic Insights on Human behavior using Image Processing",I2C2, pp.1-7, 2017.
[9] Mingqi Lv et.al , “BiView SemiSupervised Learning Based Semantic Human Activity Recognition Using Accelerometers”, IEEE Trans. on Mobile Computing, Vol: 17, Issue: 9, pp.1991-2001, 2018.
[10] Zhanxiang Feng et.al.,“Learning View Specific Deep Networks for Person ReIdentification”, IEEE Tras. on Image Processing, pp. 99, March 2018.
[11] Xiantong Zhen, et.al.,"Supervised Local Descriptor Learning for Human Action Recognition", IEEE Trans., Vol: 28, Issue:9, pp.2035 – 2047, 2017.
[12] Aasia Ali, et.al.,"Content Based Image Retrieval using Feature Extraction with Machine Learning",IEEE, ICICCS, Madurai, 2017.
[13] Krasimir Tonchev et.al.,“Digitizing human behavior in business model innovation”, IEEE Conf. on wireless, Cape Town, South Africa, 2018
[14] Xinbo Yu et.al., “Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning”, IEEE CAC, 2018
[15] S. Rajagopalan, USA, “Product Personification: Parag Model to Successful Product Development”, jmvsc. Vol. 6, No. 1, pp.1-12, March 2015.
[16] Zhanxiang Feng, et.al.,"Learning View-Specific Deep Networks for Person ReIdentification",IEEE Trans., pp. 1-12, 2018.
[17] A. Shahroudy et.al, “Deep multimodal feature analysis for action recognition in rgb+ d videos,” IEEE Tran. On Patt. Analysis and Machine Intelligence, April 2017.
[18] Julio Cesar dos Reis, “Recognizing Intentions in Free Text Messages: Studies with Portuguese Language”, IEEE 26th ICET : Infrastructure for Collaborative Enterprises (WETICE), pp.302-307, 2017

Prema P. Gawade, "A Survey on Data Analytics for Personification using Machine Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.18-21, 2019.

In recent years a lot of data has been generated owing to the exponential increase in internet usage. People are overloaded with information, spanning over multiple domains. This helps people obtain knowledge and come to informed decisions. If we consider purchasing a product as a use case, the buyer can visit multiple websites to find strengths and weaknesses of the product as well as the opinions of other purchasers. To make this process easier and faster for the buyer we propose a robust and scalable hybrid recommendation system which is implemented as a combination of Content Based Recommendation System and Collaborative Filtering Techniques. As a test case, this system has been used to help purchase smartphones based on all features and sentiments of previous purchasers. System gets data using a self-learning web crawler that gathers data from a number of relevant websites irrespective of their different structures. The sentiments of users who purchased the same items previously have also been analyzed to aid the current buyers’ decisions. In this paper, we have provided detailed survey and comparison of multiple techniques we reviewed before implementing each module.

Artificial intelligence, Decision support systems, Knowledge based systems, Hybrid intelligence systems, Data Mining, Web Mining, Supervised Learning, Recommender systems, Content based retrieval, Information retrieval, Clustering algorithms, Classification algorithms, Natural language processing, Sentiment analysis, Recurrent Neural Networks.

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[13] Shivaprasad T K, J. Shetty, “Sentiment Analysis of Product Reviews:A Review”, 2017.
[14] Suiki Li, “Sentiment analysis on Amazon Unlocked mobile phones”, 2017.
[15] Doaa Mohey, El-Din, “Enhancement Bag-of-Words Model for Solving the Challenges of Sentiment Analysis”, 2016.
[16] Xin Rong, “word2vec Parameter Learning Explained”, 2016.
[17] H. Palangi, Li Deng, Y. Shen, J. Gao, Xiaod, “Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval”, 2016.

N. Sontakke, Sabarinath S, S. Sangamnerkar, V. Iyer, "Hybrid Recommendation Engine with Web Scraping and Sentiment Analysis", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.22-31, 2019.

In Healthcare, prevention and cure have seen diverse advancement in technological schema. Chronic care and prevention care both stand on equal level with the same advancement in technology. We propose PREVASIVE method towards healthcare diagnosis. The word ‘EVASIVE’ means ‘something which is intended to come’. The prosing word ‘PERVASIVE’ is to mean prevention against the one which is intended to come. In medical history, technology contributes towards diagnosis through machine learning algorithms. Machine learning algorithms are also applied for prediction towards prevention of various diseases and this in course help for cure for specific disease. We propose diagnosis of health through inheritance traits and surroundings the person inhibits from. For knowledge, inheritance traits, history of the person is collected as PHR (Personal Health Record). The GPS (Global Positioning System) module is used to see where the person inhibits. Location and movement of person is taken into consideration to know if the region has the history of any specific diseases’ and GPS module applied with appropriate machine learning algorithms can help us determine diagnosis for diseases which are intended to come towards the specific person.

Machine learning algorithm,PHR,Healthcare,dengue,swine flu,heart diease

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[11] C. Hung, W. Chen, P. Lai, C. Lin and C. Lee, "Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, 2017, pp. 3110-3113.
[12] T. Christensen, A. Frandsen, S. Glazier, J. Humpherys and D. Kartchner, "Machine Learning Methods for Disease Prediction with Claims Data," 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, 2018, pp. 467-4674.
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[15] S. Athmaja, M. Hanumanthappa and V. Kavitha, "A survey of machine learning algorithms for big data analytics," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 2017, pp. 1-4.
[16] O. Rajabi Shishvan, D. Zois and T. Soyata, "Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey," in IEEE Access, vol. 6, pp. 46419-46494, 2018.
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Swati S. Nikam, Veena R. Pawar , Jyoti P. Kshirsagar, Ali Akbar Bagwan, "Prevasive Healthcare and Machine Learning Algorithms", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.32-36, 2019.

This paper proposes ananalytical solution for binary classification of driving behaviourinto safe or rash.A methodology is designed and developed which consists of cleaning and scrubbing of raw driving data throughidentification of best set of parameters, iterative cluster-analysis, creation of target variables by benchmarking against theoretical relations and eventually performing supervised regression using support vector machine on the prepared dataset to classify a fresh driving data point into safe or rash driving. A bad and careless driving is depicted as ‘rash driving’ while a good and efficient driving is depicted as ‘safe driving’. The proposed methodology has the potential of applying to real-world driver profiling system.

Classification, rash driving analysis, on board diagnostic, driver profiling, hierarchical clustering, principal component analysis, support vector machine

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Arushi Agrawal, Isha Gupta, "Rash Driving Detection Through Data Analytics", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.37-43, 2019.

Denial of Service attack, is one of the deadliest attacks of the Internet era. It’s major objective is to prevent legitimate users from accessing services over a network. DoS attacks can be broadly classified into network layer and application layer attacks. In this paper focus is on detection of well-known HTTP based application layer DoS attacks. We have proposed an integrated solution for detection of both volumetric and non-volumetric HTTP based application layer DoS attacks. The proposed system uses an in-memory analytics mechanism to extract the input feature set from the live traffic. On the basis of its learning from the training phase the deep neural network identifies the attacker using the feature set. We have used the TensorFlow to build the deep neural network. We have built a conformation mechanism to further reduce false positives. The result reveals that the proposed system can achieve 99.92% classification accuracy with only 0.003% false positives.

Denial of Service (DoS) Attack, Neural Network, Machine Learning, Deep Learning, Supervised Learning, Network Security, Application Layer, TensorFlow

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A.B. Mahagaonkar, A.R. Buchade, "Application Layer Denial of Service Attack Detection using Deep Learning Approach", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.44-48, 2019.

The Massive Open Online Courses (MOOCs) require extrinsic or intrinsic to the low level of completion of the training, and encounter problems with the interactivity and engagement of students throughout the MOOCs that can transform the enthusiasm of the students in boredom, and then to abandon it at any time. To attract the best players to MOOCs, gamification has been implemented on many platforms; especially since gamification became a buzzword in 2012. This research project examines the motivation and training as perceived by participants in MOOCs, using innovative educational strategies for teaching community in the field of education. In learning gamification, the use of Digital Game Learning (GBL) it provides more fun learning than gamification. In this regard, the current implementation of gamification, DGBL and their role in MOOCs will be assessed.

Gamification, Digital Game Based Learning, Massive Open Online Courses, and Online Learning

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Shivendra Chavan, Mangesh Bedekar, "Survey on MOOCs for Digital Game based Learning for Learners", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.49-53, 2019.

In recent years, generative models have gained alot of attention in the deep learning community. In particular,Generative Adversarial Networks (GANs), proposed by Ian Goodfellow et al. in 2014, and their variants have emerged as a powerful method which performs significantly better than other generative models such as Restricted Boltzmann Machines or Variational Auto-Encoders. In this paper, we focuson a specific type of GANs, the Text-to-Image GANs, and review some of the most seminal work which has been conducted in this area. We provide a high-level description of the architectural components of these models and also review their performance on variousdatasets. Further, we discuss how these architectures are suitedfor the particular use case of text-to-face image synthesis for generating images of human faces from text descriptions.

GenerativeAdversarial Networks, Text-to-ImageGANs, Deep Learning

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Siddhivinayak Kulkarni, Amol Dhondse, Anurag Katakkar, Nitish Bannur, Trupti Deshpande, "A Survey of Text-to-Image Generative Adversarial Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.07, pp.54-61, 2019.