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i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 18 Issue 2

Most Cited

Volume 16 Issue 2 October - December 2021

Research Paper

Detecting Traffic from Real Time Tweets

Aswadhati Sirisha* , K. G. Prasanthi, G. Ravikumar*

- Department of Master of Computer Applications, Vignan's Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

Department of Information Technology, Vignan's Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

Sirisha, A., Prasanthi, K. G., and Ravikumar, G. (2021). Detecting Traffic from Real Time Tweets. i-manager’s Journal on Software Engineering, 16(2), 1-8. https://doi.org/10.26634/jse.16.2.15819

Abstract

At present, one of the major issues for a person to meet their prerequisites is the cluttered traffic. Regardless of the identity, on the off chance that are out and about to face the deal, even if an individual pursues the traffic rules. So as to determine the issue, this paper concentrates on structuring an application to dole out the genuine class mark for every single individual tweet identified with the traffic words. On the off chance that any message contains traffic related data, it will be sent as an alarm to the end clients who are following the present client, or else a similar tweet will be simply posted on the client divider. In advanced times, informal organizations have turned into an intriguing space for each human being to share and convey their ongoing updates with one another. So as to actualize this application, it picks a good online life, which is Twitter.

References

[1]. Anastasi, G., Antonelli, M., Bechini, A., Brienza, S., D'Andrea, E., De Guglielmo, D., ... & Segatori, A. (2013, October). Urban and social sensing for sustainable mobility in smart cities. In 2013, Sustainable Internet and ICT for Sustainability (SustainIT) (pp. 1-4). IEEE. https://doi.org/10.1 109/SustainIT.2013.6685198

[2]. Borau, K., Ullrich, C., Feng, J., & Shen, R. (2009, August). Microblogging for language learning: Using Twitter to train communicative and cultural competence. In International Conference on Web-Based Learning (pp. 78- 87). Springer, Berlin, Heidelberg. https://doi.org/10.1007/ 978-3-642-03426-8_10

[3]. Boriboonsomsin, K., Barth, M. J., Zhu, W., & Vu, A. (2012). Eco-routing navigation system based on multisource historical and real-time traffic information. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1694-1704. https://doi.org/10.1109/TITS.2012.2204051

[4]. d'Orey, P. M., & Ferreira, M. (2013). ITS for sustainable mobility: A survey on applications and impact assessment tools. IEEE Transactions on Intelligent Transportation Systems, 15(2), 477-493. https://doi.org/10.1109/TITS.2013. 2287257

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[6]. Gonzalez, A., Bergasa, L. M., & Yebes, J. J. (2013). Text detection and recognition on traffic panels from streetlevel imagery using visual appearance. IEEE Transactions on Intelligent Transportation Systems, 15(1), 228-238. https://doi.org/10.1109/TITS.2013.2277662

[7]. Hightower, J., & Borriello, G. (2001). Location systems for ubiquitous computing. IEEE Computer, 34(8), 57-66. https://doi.org/10.1109/2.940014

[8]. Huberman, B. A., Romero, D. M., & Wu, F. (2008). Social Networks That Matter: Twitter under the Microscope. Cornell University. https://doi.org/10.48550/arXiv.0812.1045

[9]. Java, A., Song, X., Finin, T., & Tseng, B. (2007, August). Why we Twitter: Understanding microblogging usage and communities. In Proceedings of the 9^th WebKDD and 1^st SNA-KDD 2007 workshop on Web mining and social network analysis (pp. 56-65). https://doi.org/10.1145/134 8549.1348556

[10]. Kwak, H., Lee, C., Park, H., & Moon, S. (2010, April). What is Twitter, a social network or a news media? In Proceedings of the 19^th international conference on World Wide Web (pp. 591-600). https://doi.org/10.1145/17726 90.1772751

[11]. Rosi, A., Mamei, M., Zambonelli, F., Dobson, S., Stevenson, G., & Ye, J. (2011, March). Social sensors and pervasive services: Approaches and perspectives. In 2011, IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) (pp. 525-530). https://doi.org/10.1109/PER COMW.2011.5766946

[12]. Sakaki, T., Okazaki, M., & Matsuo, Y. (2012). Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering, 25(4), 919-931. https:// doi.org/10.1109/TKDE.2012.29

[13]. Tumasjan, A., Sprenger, T., Sandner, P., & Welpe, I. (2010, May). Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of the International AAAI Conference on Web and Social Media Vol. 4, No. 1, (pp. 178-185).

[14]. Wanichayapong, N., Pruthipunyaskul, W., Pattara- Atikom, W., & Chaovalit, P. (2011, August). Social-based traffic information extraction and classification. In 2011, 11^th International Conference on ITS Telecommunications (pp. 107-112). IEEE. https://doi.org/10.1109/ITST.2011. 6060036

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Research Paper

A Novel Stock Market Model using Statistical Methods

Halavath* , Alle Bhavana Kumari, D. Ram Babu*

*-*** Department of Computer Science and Engineering, Sreenidhi Institute of Science & Technology, Hyderabad, Telangana, India.

Halavath, Kumari, A. B., and Babu, D. R. (2021). A Novel Stock Market Model using Statistical Methods. i-manager’s Journal on Software Engineering, 16(2), 9-15. https://doi.org/10.26634/jse.16.2.17634

Abstract

A stock market model using statistical analysis is used to predict sales by an investment manager in such a way as to recognize the main aspects that affect the same business strategy planning. The basic understanding of the necessary data for evaluating a predictive model is achieved using statistical methods. This helps the management to define the marketing strategy while preparing a report that helps the analyst to analyze the results obtained by the marketing officer. This paper presents a theoretical and analytical framework implementation using a support vector machines for stock market forecasting. The model tries to predict whether a stock price will be higher or lower in the future or it is on a given day which helps to determine whether to invest or not. Technical and fundamental analysis or time series analysis used by most of the stockbrokers for forecasting the stocks. Machine learning algorithms implemented using Python programming language predicts the stock market. However, this paper proposes that a machine learning (ML) approach based on these aspects will use the available stock information and use the resulting information to make accurate predictions.

References

[1]. 5paisa.com. (n.d.). Introduction to Equity Stock Market in India. 5Paisa Capital Ltd. Retrieved from https://www. 5paisa.com/school/introducti on-to-stock-market

[2]. Agarwal, P. (n.d.). Introduction to the Stock Market. Intelligent Economist. Retrieved from https://www.intelligent economist.com/ stock-market/

[3]. Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014, March). Stock price prediction using the ARIMA model. In 2014, UKSim-AMSS 16^th International Conference on Computer Modelling and Simulation (pp. 106-112). IEEE. https://doi. org/10.1109/UKSim.2014.67

[4]. Ashik, A. M., & Kannan, K. S. (2019). Time series model for stock price forecasting in India. In Logistics, Supply Chain and Financial Predictive Analytics (pp. 221-231). Springer, Singapore. https://doi.org/10.1007/978-981-13- 0872-7_17

[5]. Bhuriya, D., Kaushal, G., Sharma, A., & Singh, U. (2017, April). Stock market predication using a linear regression. In 2017, International Conference of Electronics, Communication and Aerospace Technology (ICECA) (Vol. 2, pp. 510-513). IEEE. https://doi.org/10.1109/ICECA.2017. 8212716

[6]. Frankenfield, J. (2022). Machine Learning. Investopedia. Retrieved from https://www.investopedia.com/terms/m/ machine-learning.asp

[7]. Idrees, S. M., Alam, M. A., & Agarwal, P. (2019). A prediction approach for stock market volatility based on time series data. IEEE Access, 7, 17287-17298. https://doi. org/10.1109/ACCESS.2019.2895252

[8]. MathWorks. (n.d.). What Is Machine Learning? 3 things you need to know. The MathWorks, Inc. Retrieved from https://www.mathworks.com/discovery/machine-learning. html

[9]. Panigrahi, S. S., & Mantri, J. K. (2015, October). A text based Decision Tree model for stock market forecasting. In 2015, International Conference on Green Computing and Internet of Things (ICGCIoT) (pp. 405-411). IEEE. https:// doi.org/10.1109/ICGCIoT.2015.7380497

[10]. Senator With Guts (n.d.). Importance of Stock Exchange in the World Economic Setup. Senator With Guts. Retrieved from https://senatorwithguts.com/ importanceof- stock-exchange-in-the-world-economic-setup

[11]. Sharetipsinfo (n.d.). Importance of stock market and how stock market is important for countries economy. ShareTipsInfo. Retrieved from https://www.sharetipsinfo. com/economy-stock-market. html

[12]. Sunil. (2017, September 13). Understanding Support Vector Machine (SVM) algorithm from examples (along with code). Analytics Vidhya. Retrieved from https://www. analyticsvidhya.com/blog/2017/09/understaing-supportvector- machine-example-code/

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Research Paper

Online Healthcare Appointment System for Doctors and Patients

Aaliya Haque* , Sakshi Thakur, Diksha Soni Shri*

- Shri Shankaracharya Group of Institution, Junwani, Chhattisgarh, India.

Chhattisgarh Swami Vivekananda Technical University, Chhattisgarh, India.

Haque, A., Thakur, S., and Shri, D. S. (2021). Online Healthcare Appointment System for Doctors and Patients. i-manager’s Journal on Software Engineering, 16(2), 16-21. https://doi.org/10.26634/jse.16.2.18506

Abstract

This paper proposes a smart appointment booking system that provides the patients or any user with an effortless way of booking a doctor's appointment online. This is a web-based application that overcomes the issue of managing and booking appointments according to the choice or demands of the user. The task can sometimes become very tedious for the doctor's front office in manually allotting appointments for the users as per their availability. Hence, this paper offers an effective solution where users can view various booking slots available and select the preferred date and time. The already booked space will be marked yellow and will not be available to anyone else for the specified time. This system also allows users to cancel their bookings at any time. The system provides an additional feature of calculating the monthly earnings of doctors. The doctor must just feed the system regularly with daily earnings, and the system automatically generates a report of the total amount earned at the end of the month. The application uses Asp.net as a front-end and the Sol database as the back-end.

References

[1]. Aghav, J., Sonawane, S., & Bhambhlani, H. (2014, August). Health track. In 2014, International Conference on Advances in Engineering and Technology Research (ICAETR-2014) (pp. 1-5). IEEE. https://doi.org/10.1109/ICAET R.2014.7012893

[2]. Bailey, N. T. (1952). A study of queues and appointment systems in hospital out‐patient departments, with special reference to waiting‐times. Journal of the Royal Statistical Society: Series B (Methodological), 14(2), 185-199. https:// doi.org/10.1111/j.2517-6161.1952.tb00112.x

[3]. Cayirli, T., Veral, E., & Rosen, H. (2006). Designing appointment scheduling systems for ambulatory care services. Health Care Management Science, 9(1), 47-58. https://doi.org/10.1007/s10729-006-6279-5

[4]. Choudhari, S. B., Kusurkar, C., Sonje, R., Mahajan, P., & Vaz, J. (2014). Android application for doctor's appointment. International Journal of Innovative Research in Computer and Communication Engineering. 2(1), 2472-2474.

[5]. Hylton III, A., & Sankaranarayanan, S. (2012). Application of intelligent agents in hospital appointment scheduling system. International Journal of Computer Theory and Engineering, 4(4), 625-630.

[6]. Idowu, A. P., Adeosun, O. O., & Williams, K. O. (2014). Dependable online appointment booking system for NHIS outpatient in Nigerian teaching hospitals. AIRCC's International Journal of Computer Science and Information Technology, 6(4), 59-73.

[7]. Symey, Y., Sankaranarayanan, S., & Binti Sait, S. N. (2013). Application of smart technologies for mobile patient appointment system. International Journal of Advanced Trends in Computer Science and Engineering, 2(4), 74-85.

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Review Paper

A Survey on Face and Hidden Face Mask Detection

Rupali Sharma* , Abha Chaubey, Sidhhartha Chaubey*

*-*** Department of Computer Science and Engineering, Shri Shankaracharya Group of Institutions, Bhilai, Chhattisgarh, India.

Sharma, R., Chaubey, A., and Chaubey, S. (2021). A Survey on Face and Hidden Face Mask Detection. i-manager’s Journal on Software Engineering, 16(2), 22-26. https://doi.org/10.26634/jse.16.2.18515

Abstract

Coronavirus disease (COVID) is an unprecedented crisis, causing a huge amount of unhappiness and security problems. Wearing a face mask in public places can effectively reduce the transmission of coronavirus, so people should wear facial covers or masks to protect themselves from this pandemic. Therefore, this makes a facial confirmation an inconvenient task since obvious parts of the face are hidden. An important focus of analysts during the advancing COVID pandemic is to come up with ideas to address this problem with quick and productive measures. This paper proposes a reliable method based on hidden area removal and deep learning-based highlights to solve the problem of hidden face recognition measures. To deal with these challenges, it separates two unique usages in particular, such as closed-eye face detection and hidden face detection. It basically determines if a person has a mask on their face or not. It can be effectively applied transparently where a mask is needed. In contrast, the covered face affirmation means recognizing the presence of a mask on the face based on the eye area and shelter areas, as well as checking the temperature to ensure safety measures. This paper provides a survey of different research accomplished with the methodology on face mask detection with temperature check.

References

[1]. Deb, C. (n.d.). Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. GitHub. Retrieved from https://github. com/chandrikadeb7/Face-Mask-Detection.

[2]. Ge, S., Li, J., Ye, Q., & Luo, Z. (2017). Detecting masked faces in the wild with LLE-CNNs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2682-2690).

[3]. Ghose, S., & Datta, S. (2020). Automatic facial mask detection using deep learning. Mukt Shabd Journal, 9(7), 1120- 1123.

[4]. Hammoudi, K., Cabani, A., Benhabiles, H., & Melkemi, M. (2020). Validating the correct wearing of protection mask by taking a selfie: Design of a mobile application "CheckYourMask" to limit the spread of COVID-19. Computer Modeling in Engineering & Sciences, 124(3), 1049-1059, https:// doi.org/10.32604/cmes.2020.011663

[5]. Hussain, S. A., & Al Balushi, A. S. A. (2020). A real time face emotion classification and recognition using deep learning model. Journal Of Physics: Conference Series, 1432(1), 012087. https://doi.org/10.1088/1742-6596/143 2/1/012087

[6]. Jiang, M., Fan, X., & Yan, H. (2020). Retinamask: A face mask detector. arXiv preprint arXiv:2005.03950. https:// doi.org/10.48550/arXiv.2005.03950

[7]. Lin, K., Zhao, H., Lv, J., Li, C., Liu, X., Chen, R., & Zhao, R. (2020). Face detection and segmentation based on improved mask R-CNN. Discrete Dynamics in Nature and Society, 2020, Article ID 9242917, 1-11. https://doi.org/10.1155/2020/9242917

[8]. Meenpal, T., Balakrishnan, A., & Verma, A. (2019, October). Facial mask detection using semantic segmentation. In 2019, 4^th International Conference on Computing, Communications and Security (ICCCS) (pp. 1-5). IEEE. https://doi.org/10.1109/CCCS.2019.8888092

[9]. Said, Y. (2020). Pynq-YOLO-Net: An embedded quantized convolutional neural network for face mask detection in COVID-19 pandemic era. International Journal of Advanced Computer Science and Applications, 11(9), 100-106.

[10]. Vinitha, V., & Velantina, V. (2020). COVID-19 facemask detection with deep learning and computer vision. International Research Journal of Engineering and Technology, 7(8), 3127-3132.

[11]. Yadav, S. (2020). Deep learning based safe social distancing and face mask detection in public areas for COVID-19 safety guidelines adherence. International Journal for Research in Applied Science and Engineering Technology, 8(7), 1368-1375.

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Review Paper

A Study on Cyber Security Challenges and its Protections in the Current Scenario

Shameemul Haque* , Mudassir Peeran**

* Department of Computer Science, King Khalid University, Saudi Arabia.

** Department of Information System, University of Bisha, Saudi Arabia.

Haque, S., and Peeran, M. (2021). A Study on Cyber Security Challenges and its Protections in the Current Scenario. i-manager’s Journal on Software Engineering, 16(2), 27-30. https://doi.org/10.26634/jse.16.2.15598

Abstract

In the present era, cybersecurity has become an integral part of information technology. The biggest problem right now is how to protect the information. Since cybercrime has become a reality, it is more important to consider the different ways in which, cybersecurity can protect the information. Governments and organizations have begun to take precautionary measures to prevent these cybercrimes. This paper, mainly focuses on cyber security in the latest technologies, practices and changing trends.

References

[1]. Capital Enterprise. (2018). Cybersecurity: Trends and Developments. Retrieved from https://medium.com/ capital-enterprise/cybersecurity-trends-and-develop ments-in-2018-e295a18d8ee

[2]. Fireeye. (n.d.). What is Cyber Security? Retrieved from https://www.fireeye.com/current-threats/what-is-cybersecurity. html

[3]. Godbole, N., & Belapure, S. (2011). Cyber Security: Understanding Cyber Crimes, Computer Forensics and Legal Perspectives Wiley-India. (pp. 1-636).

[4]. Mehling, H. (2011). 3 Reasons Why Security Appliances are Hot. eSecurity Planet. Retrieved from https://www. esecurityplanet.com/trends/article.php/3933646/3- Reasons-Why-Security-Appliances-are-Hot.htm

[5]. Press, G. (2018, December 3). 60 Cybersecurity Predictions For 2019. Retrieved from https://www.forbes. com/sites/gilpress/2018/12/03/60-cybersecurity-predic tions-for-2019/

[6]. Richardson, R., & North, M. M. (2017). Ransomware: Evolution, mitigation and prevention. International Management Review, 13(1), 10.

[7]. Tannam, E. (2019). Cybersecurity Trends 2019: What should you look out for? Retrieved from https://www.silicon republic.com/enterprise/cybersecurity-trends-iot

[8]. Testbytes. (2020, January 20). Major Cyber Attacks on India (Exclusive News). Retrieved from https://www.test bytes.net/blog/cyber-attacks-on-india/

[9]. Vidhya, P. M. (2014). Cyber security - Trends and challenges. International Journal of Computer Science and Mobile Computing, 3(2), 586-590.

i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 18 Issue 2

Most Read

Most Cited

Volume 16 Issue 2 October - December 2021

Detecting Traffic from Real Time Tweets

Aswadhati Sirisha* , K. G. Prasanthi**, G. Ravikumar***

*-** Department of Master of Computer Applications, Vignan's Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India. *** Department of Information Technology, Vignan's Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

Sirisha, A., Prasanthi, K. G., and Ravikumar, G. (2021). Detecting Traffic from Real Time Tweets. i-manager’s Journal on Software Engineering, 16(2), 1-8. https://doi.org/10.26634/jse.16.2.15819

Abstract

References

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A Novel Stock Market Model using Statistical Methods

Halavath* , Alle Bhavana Kumari**, D. Ram Babu***

*-*** Department of Computer Science and Engineering, Sreenidhi Institute of Science & Technology, Hyderabad, Telangana, India.

Halavath, Kumari, A. B., and Babu, D. R. (2021). A Novel Stock Market Model using Statistical Methods. i-manager’s Journal on Software Engineering, 16(2), 9-15. https://doi.org/10.26634/jse.16.2.17634

Abstract

References

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Online Healthcare Appointment System for Doctors and Patients

Aaliya Haque* , Sakshi Thakur**, Diksha Soni Shri***

*-** Shri Shankaracharya Group of Institution, Junwani, Chhattisgarh, India. *** Chhattisgarh Swami Vivekananda Technical University, Chhattisgarh, India.

Haque, A., Thakur, S., and Shri, D. S. (2021). Online Healthcare Appointment System for Doctors and Patients. i-manager’s Journal on Software Engineering, 16(2), 16-21. https://doi.org/10.26634/jse.16.2.18506

Abstract

References

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A Survey on Face and Hidden Face Mask Detection

Rupali Sharma* , Abha Chaubey**, Sidhhartha Chaubey***

*-*** Department of Computer Science and Engineering, Shri Shankaracharya Group of Institutions, Bhilai, Chhattisgarh, India.

Sharma, R., Chaubey, A., and Chaubey, S. (2021). A Survey on Face and Hidden Face Mask Detection. i-manager’s Journal on Software Engineering, 16(2), 22-26. https://doi.org/10.26634/jse.16.2.18515

Abstract

References

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A Study on Cyber Security Challenges and its Protections in the Current Scenario

Shameemul Haque* , Mudassir Peeran**

* Department of Computer Science, King Khalid University, Saudi Arabia. ** Department of Information System, University of Bisha, Saudi Arabia.

Haque, S., and Peeran, M. (2021). A Study on Cyber Security Challenges and its Protections in the Current Scenario. i-manager’s Journal on Software Engineering, 16(2), 27-30. https://doi.org/10.26634/jse.16.2.15598

Abstract

References

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Aswadhati Sirisha* , K. G. Prasanthi, G. Ravikumar*

- Department of Master of Computer Applications, Vignan's Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

Department of Information Technology, Vignan's Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

Halavath* , Alle Bhavana Kumari, D. Ram Babu*

Aaliya Haque* , Sakshi Thakur, Diksha Soni Shri*

- Shri Shankaracharya Group of Institution, Junwani, Chhattisgarh, India.

Chhattisgarh Swami Vivekananda Technical University, Chhattisgarh, India.

Rupali Sharma* , Abha Chaubey, Sidhhartha Chaubey*

* Department of Computer Science, King Khalid University, Saudi Arabia.

** Department of Information System, University of Bisha, Saudi Arabia.