iosr-JCE   Volume-23 ~ Issue-3 ~ MAy - June 2021

Abstract: In this paper, we have designed a method for retrieving web information using selenium and python script. Selenium is used to automate web browser interaction. The block-based structure is obtained by using a python script. Web information is mostly unstructured, the proposed work helps to organize the unstructured data and make it useful for various data analysis techniques. It also focuses on ways in which data can be persisted and used from various websites for which APIs are not available.

Key Word: Web scraping; Selenium; Information Extraction; HTML Parsing; Web data retrieval

[1]. Anand V. Saurkar, Kedar G. Pathare, Shweta A. Gode, "An overview of web scraping techniques and tools" International Journal on Future Revolution in Computer Science and Communication Engineering, April 2018.
[2]. Jiahao Wu, "Web Scraping using Python: Step by step guide" ResearchGate publications (2019).
[3]. Matthew Russell, "Using python for web scraping," No Starch Press, 2012.
[4]. Ryan Mitchell, "Web scraping with Python," O'Reilly Media, 2015.
[5]. Selenium Library : https://pypi.org/project/selenium/..

Abstract: Information and computer security is supported largely by passwords which are the principle part of the authentication process.Authenticating by entering a PIN (numerical password) is the most common authentication technique these days. ATM, Mobile application passwords, POS terminals, electronic door access system, etc. mostly use PIN as their authenticating mechanism. As these passwords are to be typed in frequently and always in a hurry, their length is too small and hence, any person or camera can easily scan them and make a note of it. This makes it too unsecure as they are highly hack able. Shoulder surfing attack is the most common type of hacking attack among these types of hacks. The authentication system we are proposing here is resistant to.....

Key words: Authentication, shoulder surfing attack, graphical pin entry

[1]. J. Bonneau, C. Herley, P. C. v. Oorschot, and F. Stajano, "The quest to replace passwords: A framework for comparative evaluation of web authentication schemes," in 2012 IEEE Symposium on Security and Privacy, May 2012, pp. 553–567.
[2]. M. Harbach, A. De Luca, and S. Egelman, "The anatomy of smartphone unlocking: A field study of android lock screens," in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, ser.CHI '16. New York, NY, USA: ACM, 2016, pp. 4806–4817.
[3]. J. Bonneau, S. Preibusch, and R. Anderson, "A birthday present every eleven wallets? the security of customer-chosen banking pins," in Financial Cryptography and Data Security, A. D. Keromytis, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 25–40.
[4]. M. Harbach, E. Von Zezschwitz, A. Fichtner, A. De Luca, and M. Smith,"It's a hard lock life: A field study of smartphone (un)locking behavior and risk perception," in Proceedings of the Tenth USENIX Conference on Usable Privacy and Security, ser. SOUPS'14. Berkeley, CA, USA:USENIX Association, 2014, pp. 213–230.
[5]. T. Kwon, S. Shin, and S. Na, "Covert attentional shoulder surfing: Human adversaries are more powerful than expected," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 6, pp. 716–727,June 2014

Abstract: he World Wide Web has evolved into a powerful knowledge resource as well as a collaborative business forum. Web mining is a technique for extracting data from the internet using data mining techniques and algorithms, such as Web documents and software, links, web content, and server logs. Building a website is, in reality, a challenging task. Designing a website nowadays is a difficult task.
You will learn how your website is communicated, organised and displayed via Web servery logs. Therefore, in this article we discuss the need for a complex data extraction process based on previous mining performances to explore user access trends. Based on the latest data mining findings and updates to web server logs, we suggest an effective approach to new mining legislation.

Keywords: Information Discovery, Data Mining, Web Mining, User Access Patterns, Association Mining, and Web Structure

[1]. RAYMOND KOSALA, HENDRIK BLOCKEEL, Web Mining Research: A Survey, Sigkdd Expirations, AcmSigkdd, July2000.
[2]. M. KOSHER. ALIKE - Archie-Like Indexing In The Web. In Proc. 1st International Conference On The World Wide Web, Pages 91--100, May 1994.
[3]. R. COOLEY, B. MOBASHER, AND J. SRIVASTAVA. Web Mining: Information And Pattern Discovery On The World Wide Web. In Proceedings Of The 9th Ieee International Conference On Tools With Artificial Intelligence (Ictai'97), 1997
[4]. R. KOSALA, H. BLOCKEEL. Web Mining Research: A Survey Data & Knowledge Engineering, Volume 53, Issue 3, June 2005, Pages 225-241
[5]. NASRAOUI, O. ET AL. , A Web Usage Mining Framework For Mining Evolving User Profiles In Dynamic Web Sites, IeeeTransactions On Knowledge And Data Engineering, Volume: 20 Issue:2 On Page(S): 202 – 215, 2008.

Abstract: Network Intrusion Detection System is a software system used to detect security threats, vulnerabilities and anomaly activities in the computer networks and host-based environments. In the past decade, several researches have been proposed by various authors. However, due to the rapid advancement of technology, still it is being a challenging research area. In this paper, we have analyzed the performance of NSL-KDD 20% dataset in different standard mechanisms. We found the existing techniques are biased due to the imbalanced datasets. We have selected the ensemble techniques in order to enhance the efficiency of intrusion detection. We have proposed ensemble models like bagging, boosting and stacking classifiers to improve the efficiency of intrusion detection techniques. Finally, we have achieved better results for NSL-KDD Dataset using a stacking classifier with cross-validation.

Key Words: Attacks, Data Sets, Ensemble Approach, Intrusion Detection System, Machine Learning

[1]. Shameli-Sendi, A., Cheriet, M., Hamou-Lhadj, A., 2014. Taxonomy of intrusion risk assessment and response system. Comput. Secur. 45, 1–16.
[2]. Moustafa, N., Slay, J., Creech, G., 2017. Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks. IEEE Transactions on Big Data 1–14.
[3]. Moustafa, N., Creech, G., Slay, J., 2018a. Anomaly detection system using beta mixture models and outlier detection. In: Progress in Computing, Analytics and Networking. Springer, pp. 125–135.
[4]. Pontarelli, S., Bianchi, G., Teofili, S., 2013. Traffic-aware design of a high-speed fpga network intrusion detection system. IEEE Trans. Comput. 62 (11), 2322–2334.
[5]. Wang, L., Jones, R., 2017. Big data analytics for network intrusion detection: a survey. Int. J. Network. Commun. 7 (1), 24–31.

Abstract:"Patter recognition" is the Scientific discipline of machine learning (or Artificial Intelligence) that aims at classifying data (Patterns) into a number of categories of classes." Now what is "Pattern"
1) A "Pattern" is an object, process or event that can be given a name. 2) A Pattern is a set of patterns sharing common attributes and usually originating from the same source. So, Pattern Recognition may be characterized as an information reduction information mapping, or information labeling process. The objective of this paper is a to "Automatic speech and speaker recognition." This research directions will emerge and new techniques......

Key words: Postulates of PR, Evaluation of classifiers, PR Process Acoustic Model output

[1]. Richard O. Duda, Peter E. Hart, David G. Stock : Pattern Classification, 2nd edition, John Wiley and Sons, New York 2001, EUR 114.46
[2]. Tervor Hastie, Robert Tobshirani Jerome Friedman : The Elements of Statistical Learning – Data Mining, Inference and Prediction, 2nd edition, Springer, New York, 2009, FUR 71:05
[3]. http: www- Stat. Stanford., edu/vtibs/Elem Stat Leann
[4]. Christopher M. Bishop :
[5]. Pattern Recognition and Machine learning, Springer, New York, 2006, EUR 75.05.

Abstract: Background: The institutions and organization generally use fingerprint recognition and signature-based attendance system to mark the attendance. The fingerprint recognition system is secure and reliable but in both the process user has to get in contact with the surface of the attendance system, which can cause the spread of viruses and bacteria among the people, for example, diseases like COVID-19, influenza and the common cold can spread through getting in contact with a contaminated surface. So, in the pandemic of COVID-19 and other future infections, the uses of such systems are not feasible. For that, this paper presents the model which is secure, reliable and contactless. The model is consist of a serially connected face recognition and Radio Frequency Identification (RFID) module for marking the attendance, an Infrared thermal sensor for scanning body temperature and FAR......

Key Word: Facial Recognition, RFID, IR Thermal Sensor, FAR Ultraviolet-C.

[1] Md. Sajid Akbar, Jia Uddin, "Face Recognition and RFID Verified Attendance System," IEEE, 978- 1-5386-4904-6/18/$31.00, 2018.
[2] Srivignessh Pss Bhaskar. M, "RFID and Pose Invariant Face Verification Based Automated Classroom Attendance System," IEEE, 978-1-4673-6621-2/16/$31.00, 2016.
[3] Marie Lindblad, Eva Tano, Claes Lindahl, Fredrik Huss, "Ultraviolet-C decontamination of a hospital room: Amount of UV light needed,". Published by Elsevier Ltd., 0305-4179, 2019.
[4] Gonçalo Marques and Rui Pitarma, "Non-contact Infrared Temperature Acquisition System based on Internet of Things for Laboratory Activities Monitoring,". The 9th International Conference on Sustainable Energy Information Technology (SEIT), Published by Elsevier B.V, 155 (2019) 487–494, 2019.
[5] Jing Zhang, "Development of a Non-contact Infrared Thermometer,". International Conference Advanced Engineering and Technology Research, 2017.

Abstract: COVID-19 pandemic has a devastating effect on many aspects of human life. Throughout the early months of 2020, COVID-19 rapidly changed how the world functioned, with the closure of borders, schools and workplaces, national lockdowns, and the rapid normalization of "self-isolation" and "social distancing". However, while public education system recommendations were broadly universal, human capacity to accordingly transform everyday life has differed significantly. The access to basic infrastructure is limited, and where overcrowding and high density are the norm, it is frequently impossible to transform daily life in the required ways. The failure of global education system recommendations to recognize these inequalities, and to adapt advice to national and local contexts, reveals significant limitations that extend beyond this specific global pandemic. Education sector is most affected in numerous ways of disciplines are no exception to it. Education has an essential role in human life..

Key Word: Covid-19, Pandemic,Impact on Education System, Online Training, Electrical Devices

[1]. World Health Organization (2020). Coronavirus disease (COVID-19) Pandemic, WHO. Accessed from https://www.who.int/emergencies/diseases/novel-coronavirus-2019 on 31stMarch 2020
[2]. Sohrabi C., Alsafi Z., O'Neill N., Khan M, Kerwan A Al-Jabir A., Iosifidis C., Agha R. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19) Int. J. Surg. Lond.Engl.2020;76:71–
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[3]. Royal College of Surgeons of England Guidance for surgeons working during the COVID-19 pandemic. 2020. https://www.rcseng.ac.uk/coronavirus/joint- guidance-for-surgeons-v1/ accessed April 2, 2020.
[4]. WHO Director-General's opening remarks at the media briefing on COVID-19 - March 11 2020. (https://www.who.int/dg/speeches/detail/who - director-general-s-opening-remarks-at-the-media- briefing-on-covid-19---11-march-2020).
[5]. Al-Jabir A, Kerwan A, Nicola M, et al. Impact of the Coronavirus (COVID-19) pandemic on surgical practice - Part 1. Int J Surg. 2020;79:168-179. doi:10.1016/j.ijsu.2020.05.022..

Abstract: In many applications of today's information systems, Mining and analyzing data streams is inevitable in order to find the useful insights from the data streams . At present , many researchers have extensively worked on this area of research, However, there are specific problems that challenge the ongoing algorithmic solutions . Hence, this area of research is evolving and gaining extensive attention as the work progresses . The aim of this paper is to present a comprehensive spectrum of various approaches used for data streams through systematic and deep dive into the relevant literature . The Paper also discusses the advanced learning concepts, novel and adapted algorithms, summarizing major characteristics and highlighting important drawbacks. The Paper concludes with key challenges faced by algorithmic solutions, discussion of open research problems and lines of future research.

Key Word: Data streams, stream mining ,real time big data, incremental learning

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