iosr-JCE   Volume-22 ~ Issue-4 ~July - Aug. 2020

Abstract: Object/pattern recognition is a technique of computer vision for image or video classification of objects. Facial recognition is one of the most popular streams of object recognition. Facial recognition is an automated classification that mathematically finds a person's attributes regarding the face using deep learning.This paper elucidates the phases of facial detection, extraction of facial features, and finally identification of the person based on said features.As concepts of facial recognition become more and more relevant in the modern industry, researchers have created and refined many recognition models. A few of the drawbacks of these facial recognition solutions are their heavy training times and limited reusability.This paper addresses these problems and attempts to solve them by creating an ensemble of best in the market technologies to achieve high accuracy at high speeds. The system is designed to allow fast customization for any niche dataset, for example, students attending a class, a group of wanted individuals, patients in a hospital, etc. Background:Computer vision and........

Keywords: Facial Recognition; Google FaceNet; YOLOv3; Computer Vision; Image Processing; Deep Learning.

[1]. Wiley, Victor & Lucas, Thomas. (2018). Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research.2.22.10.29099/ijair.v2i1.42.program (NCEP) expert panel on detection, evaluation, and treatment of highblood cholesterol in adults (adult treatment panel III) finalreport. Circulation. 2002;106(25, article 3143).
[2]. Wu, Juan & Peng, Bo & Huang, Zhenxiang&Xie, Jietao. (2013). Research on Computer Vision-Based Object Detection and Classification. IFIP Advances in Information and Communication Technology. 392. 183-188. 10.1007/978-3-642-36124-1_23.
[3]. Gupta, Abhishek. (2019). Current research opportunities of image processing and computer vision. Computer Science. 20. 10.7494/csci.2019.20.4.3163.
[4]. Anwarul, Shahina&Dahiya, Susheela. (2020). A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy. 10.1007/978-3-030-29407-6_36.
[5]. Lal, Madan & Kumar, Kamlesh&Arain, Rafaqat&Maitlo, Abdullah &Ruk, Sadaquat& Shaikh, Hidayatullah. (2018). Study of Face Recognition Techniques: A Survey. International Journal of Advanced Computer Science and Applications. 9. 10.14569/IJACSA.2018.090606...

Abstract: Information and Communication Technology is being increasingly used in day to day life of a common man and it is rapidly becoming an integral part of providing the better governance services to the citizens of a country. e-Governance aims to provide good governance to the public by using the Information and Communication Technology (ICT) for speedy, accurate, transparent and secured services. e-Governance tries to eliminate the digital divide among urban and rural people. Timely and efficient delivery of e-Governance services is an important aspect. Now a days, departments, business sectors and customers alike collect, store and transmit vast amount of information electronically......

[1]. Shaikh Imtiyaj, Er Ratan Kumar Agrawal, Dr A K Hota "Study of Online Banking Solution for e-Governance initiatives: e-Procurement-A Scientific Knowledge", IOSR Journal of Computer Engineering(IOSR-JCE) e-ISSN:2278-0661,p-ISSN:2278-8727,Volume 19,Issue 2,Ver.I V(Mar-Apr. 2017), PP 55-61
[2]. Shaikh Imtiyaj, Er Govinda Chandra Mangual, Dr A K Hota "Study of e-Governance initiatives:e-Procurement a Business Reform for Odisha's Development", IOSR Journal of Computer Engineering(IOSR-JCE) e-ISSN:2278-0661,p-ISSN:2278-8727,Volume 18,Issue 6,Ver. I(Nov-Dec. 2016), PP 44-53
[3]. Shaikh Imtiyaj, Er Govinda Chandra Mangual, "An Indepth Understanding of e-Governance initiatives: e-Procurement –A Great Success in Odisha", IOSR Journal of Computer Engineering(IOSR-JCE) e-ISSN:2278-0661,p-ISSN:2278-8727,Volume 18,Issue 4,Ver. V(Jul-Aug. 2016), PP 144-147
[4]. Shaikh Imtiyaj, N.R Biswal, T.P Ray, Dr A.K Hota, "An Indepth Understanding of e-Procurement : A Case Study Approach", IOSR Journal of Computer Engineering(IOSR-JCE) e-ISSN:2278-0661,p-ISSN:2278-8727,Volume 17,Issue 6,Ver. V(Nov-Dec. 2015), PP 20-24
[5]. Shaikh Imtiyaj, N.R Biswal, T.P Ray, Dr A.K Hota , "Digital Signature Certificate: A blessing for e-Governance Application in Human Development", International Journal of Advanced Research in Science, Engineering and Technology,Vol. 2, Issue 1 , pp 350-355,January 2015.

Abstract: This paper shows a comparison and style of a sensible device – sensible Mirror. The sensible mirror mentioned here is especially for home surroundings. This paper highlights a number of the sensible mirrors from completely different firms. These sensible mirrors don't seem to be wide used because of price or high needs of hardware. The projected sensible mirror are operated by Raspberry Pi and can be connected by planet through net. The sensible mirror can consist Raspberry Pi, junction rectifier monitor, speakers, camera, electro-acoustic transducer with two-way mirror and acrylic glass. With the assistance of voice recognition api the mirror can communicate with the user through voice commands and responds them consequently. The mirror may conjointly support human gestures. The mirror can highlight some basic amenities like time, local news, weather. The mirror also will perform some advance functions like booking a ride on Uber, face recognition, etc. This mirror with computing can give a unprecedented expertise to the user.

Keywords: Artificial Intelligence, Raspberry Pi, Smart Mirror, Machine Learning, Home Automation

[1]. D.K.Mittal – a comparative study and a new model for smart mirror-International journal of scientific research in computer science and engineering -volume 5.
[2]. https://github.com/aishmittal/Smart-Mirror
[3]. https://github.com/MichMich/MagicMirror
[4]. Microsoft Smart mirror
[5]. ekko smart mirror

Abstract: Background:The healthcare sector has huge amount of medical data which are information rich and still not properly analyzed; especially, discovering useful information to predict future patterns is very limited. By using data mining techniques, the current study introduced a novel classification methodology and successfully applied it in Sri Lankan domain for the Chronic Kidney Disease (CKD) classifications. Methods: This study is carried under the two phases. In the first phase, Artificial Neural Network (ANN) method namely multilayer feed-forward neural network was used to detect whether a person has a risk of having a kidney disease or not and their risk level. In the second phase, a novel forecasting methodology is proposed using multiple algorithms, which is a.....

Keywords: Artificial Neural Network, Data Mining, Kidney Disease, Random Forest

[1]. Srinivas, K., Rani, B.K. and Govrdhan, A., "Applications of data mining techniques in healthcare and prediction of heart attacks.," International Journal on Computer Science and Engineering, vol. 2, no. 02, pp. 250-255, 2010.
[2]. Kraft, M.R., Desouza, K.C. and Androwich, I.,, "Data mining in healthcare information systems: case study of a veterans' administration spinal cord injury population," in 36th Annual Hawaii International Conference on System Sciences, 2003.
[3]. Jothi N, Husain W, "Data mining in healthcare–a review.," in Procedia Computer Science, 2015.
[4]. Abdel-Kader K, Unruh ML, Weisbord SD, "Symptom burden, depression, and quality of life in chronic and end-stage kidney disease.," Clinical Journal of the American Society of Nephrology, vol. 4, no. 6, pp. 1057-1064, 2009.
[5]. Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, Levin A, Levey AS, "A predictive model for progression of chronic kidney disease to kidney failure," Jama, vol. 305, no. 15, pp. 1553-1559, 2011.

Abstract: Computer Vision Syndrome is the combination of eye and vision problems associated with the use of computers. A quantitative descriptive study was conducted with students on Asante Akyem Campus, Agogo of the Presbyterian University College with the use of questionnaires. Prolonged and continuous use of computer usually leads to Computer Vision Syndrome and result to low productivity. The objectives of this study is to find out the prevalence of Computer Vision Syndrome among university students, to assess the knowledge of university students on Computer Vision Syndrome, to identify if students are knowledgeable on the preventive measures of CVS using a sample of 306 respondents. The results obtained revealed that, majority of respondents (52.1%) indicated backache, neck pain/shoulder (55.9%),.....

Keywords: Computer Vision Syndrome, Prevalence, Knowledge, Computer

[1]. Ahmed, S. (2009). Methods in Sample Surveys. John Hopkins University.
[2]. Akinbinu, T. R. (2013). Knowledge Of Computer Vision Syndrome Among Computer Users In The Workplace In Abuja, Nigeria. Dissertation, 1-90.
[3]. Akinbinu, T. R., & Mashalla, Y. J. (2014). Impactof computer technology on health: Computer vision Syndrome. Academic Journal, 1-6.
[4]. American Optometric Association. (2005, June 11). Caused-By-My-Your-Computer. The effects of Video Display Terminal Use One, 11-21.

Abstract: The storage and handling of passwords is a vital part of security systems. We introduce a novel approach for password authentication to store and handle passwords securely, which could be seamlessly, merged with existing security systems. This approach protects the genuine plaintext password from being discovered by a malicious actor even if the database storing the passwords suffers a data breach. The data breach could be a result of an unpatched or zero-day vulnerability. A plaintext password is hashed using a hash function. The hash is then split into two parts. One part is used to create a series of patterns (or) Anti-passwords involving the use of an Anti-password algorithm, which....

Keywords: Advanced Encryption Standard (AES); Anti-password; Negative Database (NDB); Hybrid password authentication system (HPAS).

[1]. Fernando Esponda, ―Everything that is not important: Negative databases‖, IEEE Computational Intelligence Magazine, vol. 3, no. 2, pp. 60-63, 2008.
[2]. Emin İslam Tatl, ―Cracking More Password Hashes with Patterns‖, IEEE Transactions on Information Forensics and Security, vol.10, no. 8, pp. 1656-1665, 2015.
[3]. Hung-Min Sun, Yao-Hsin Chen and Yue-Hsun Lin, ―oPass: A User Authentication Protocol Resistant to Password Stealing and Password Reuse Attacks‖, IEEE Transactions on Information Forensics and Security, vol. 7, no. 2, pp. 651-663, 2012.
[4]. Liang and J, Lai, X, ―Improved Collision Attack on Hash Function MD5‖, Journal of Computer Science & Technology vol. 22, pp. 79–87, 2007.
[5]. Chao Gong and Brandon Behar, ―Understanding Password Security through Password Cracking‖, Journal of Computing Sciences in Colleges, Evansville, United States, vol. 33, no. 5 pp.81-87, 2018

Abstract: This main aim of this paper is to develop a Secure and decentralised smart election system using blockchain technology. Over the last few years the traditional election processes have come under a lot of scrutiny whether they are secure or not. They are not fully secure as it is easy to attack the places where elections are being held which is called 'booth capturing'. It also threatens privacy of voters. In recent years the attack on the current election process has been widespread and there is demand to make it more secure and more transparent. For decades Electronic voting systems have been a topic of interest, as there is a need for alternative processes to carry out an election. The main goal is to minimize the cost of conducting an election, while maintaining the election standards with respect to security and privacy, while having the voters an option of voting from any place, with any device...

Index Terms: Blockchain, Smart contract, Ethereum, Solidity, Decentralized System

[1]. Blockchain-based e-Vote-as-a-Service, Emanuele Bellini1 IEEE Member, Paolo Ceravolo2 IEEE Member, Ernesto Damiani IEEE Member,2019 IEEE 12th International Conference on Cloud Computing (CLOUD)
[2]. Blockchain-Based E-Voting System, Friðrik Þ. Hjálmarsson, Gunnlaugur K. Hreiðarsson, Mohammad Hamdaqa, Gísli Hjálmtýsson School of Computer Science Reykjavik University, Iceland,2018 IEEE 11th International Conference on Cloud Computing.
[3]. Securing E-Voting Based On Blockchain in P2P Network, Haibo Yi, EURASIP Journal on Wireless Communications and Networking 2019, Springer.
[4]. Secure Electronic Voting System Using Blockchain Technology , Ashish Singh, Kakali Chatterjee, International Conference on Computing, Power and Communication Technologies 2018, IEEE.
[5]. Votereum: An Ethereum-Based E-Voting System, Linh Vo-Cao- Thuy, Khoi Cao-Minh , Chuong Dang-Le-Bao, Tuan A. Nguyen, IEEE-RIVF International Conference on Computing and Communication Technologies 2019

Abstract: In this Research investigation we présent new results in two areas – Cryptographic protocols and Lattice Problems. We introduce a new Protocolfor electronic cash which is exclusively designed to fonction on hardware with limited computing power. The approche has provable Security properties and low coputational requiremnts, but it still gives a fair amount of privacy. Major feature of the system is that there is no master secret that could be used for counterfeiting money if stolen. In this Research content we introduce the notion of hierarchial group signatures. In Merchant transactional process the signer that is as similar as a leaf of the sub tree of a group manager, the....

Key Word: Asymmetric, Cryptographic protocols, electronic cash, Counterfeiting, encryption, signers, trapdoor..

[1]. J.Camenisch.Efficient and generalized group signature. In Advances in Cryptozoology – EUROCRYPT-97, Vol.1233 of Lecture Notes in Computer Sciences, Pages 465-479,Springer Verlag,1997.
[2]. J-Y Cai and A,Nerukar, An improved worst case to average case connection for lattice problems. In 38th IEEE Symposium on ACM Symposium on the Theory of Computing (STOC), pages 468-477. IEEE Computer Society Press,1997.
[3]. L.Chen and T.P.Pedersen, New group Signature schemes. In Advances in Cryptology, Springer Volume 1994.
[4]. R.Cromer, I Damgard, and B.Schoenmakers. Proofs of partial knowledge and simplified diesing of witness hiding protocols. In Advances in Cryptology – CRYPTO-94 volume 839 of Lecture Notes in Computer Science, Pages:174-187, Springer Verlag,1994.
[5]. The proth search page: http:// www.prothsearch.net,March 2004.

Abstract: Raakhee is a smart ring developed to secure or make aware the victim from any sudden situation like child trafficking, women security, or any sort of panic or medical attack. The smart ring is designed -with an emergency situational task keeping in mind such that there are many android applications developed for person's safety, though it not be possible to carry mobile phone at the situation. The device contains different modules such as GPS (Global positioning system), GSM, camera, buzzer, shock mechanism circuit. The designing of the ring has a combination of web technology, electronic, UI design. The project started by obtaining a raspberry pi and calibrating it with software. Then a suitable ring was searched withshock technology as well as some sensors to physically interact with device. Finally, the frame for the concept is designed to give it a final look. I used adobe XD for the UI design, web technology tools for software and electroniccollaboration.

[1]. Jiahui Wu, Gang Pan, Daqing Zhang, Shijian Li, Zhaohui Wu, MagicPhone: Pointing & Interacting. In Proc. of UbiComp 2010, 451 - 452.
[2]. David Fleer, Christian Leichsenring, MISO: A Context-Sensitive Multimodal Interface for Smart Objects Based on Hand Gestures and Finger Snaps. In Proc. of UIST 2012, 93 - 94.
[3]. Yoon S. H., Zhang Y., Huo K., Ramani K., TRing: Instant and Customizable Interactions with Objects Using an Embedded Magnet and a Finger-Worn Device, In UIST '16.
[4]. Prof. R.A. Jain, Aditya Patil, PrasenjeetNikam, Shubham More, Saurabh Totewar," Women's safety using IOT ". Vol: 04 Issue: 05| May-2017
[5]. Prof. R.A. Jain, Aditya Patil, PrasenjeetNikam, Shubham More, Saurabh Totewar," Women's safety using IOT ". Vol: 04 Issue: 05| May-2017

Abstract: Over the years, there has been lot of changes that has taken place which affects human life. Due to increase in the number of the vehicles in cities have led to major problems such as traffic, pollution, etc. One such problem that is the reason for traffic and disturbance is the parking problem. People usually park their vehicle in an indiscipline manner which leads to informality. Also people usually waste their time and efforts for searching proper space to park their vehicle. The aim of this paper is to resolve this issue related to parking. IoT based system named Automated Parking is implemented which involves sensors for detection of free spaces and LCD's for display purpose. RFID tag for payment purpose. This system allows the user to get the nearest free slot for parking which avoids the wastage of the time and also avoids traffic

[1]. J . Cynthia, C.BharathiPriya, & P. A. Gopinath (November 2018) . IOT based Smart Parking Management System. International Journal of Recent Technology and Engineering.
[2]. JayaksheiDadajiBachhav, &Prof.Mechkul M. A. Smart Car Parking system (IRJET) 2019.
[3]. Yang, J., Portilla, J., &Riesgo, T. (2012, October). Smart parking service based on wireless sensor networks. In IECON 2012-38th Annual Conference on IEEE Industrial Electronics.
[4]. Patil, M., &Bhonge, V. N. (2013). Wireless sensor network and RFID for smart parking system. International Journal of Emerging Technology and Advanced Engineering, 3(4),188-192.
[5]. https://www.circuitstoday.com

Abstract: Nowadays, there is remarkable growth in technology and wireless sensor networks. These are primarily used for communication. The medium of communication between devices may be wired or wireless, hence, the chance of attacks through the networks is increasing daily. For secure communication, intrusion detection and prevention are primary concerns. Thus, study and analyses of intrusion detection and prevention techniques are the necessity to secure the network. With the assistance of intrusion detection and prevention systems, we can determine and then notify the normal and abnormal activities of the users. Thus, there is a requirement to design effective intrusion detection....

[1] Rakesh Sharma, and Vijay Anant Athavale, "A Survey of Intrusion Detection Techniques and Architectures in Wireless Sensor Networks", Int. J. Advanced Networking and Applications, Volume: 10 Issue: 04 Pages: 3925-3937, 2019.
[2] M. Ngadi, A.H. Abdullah, and S. Mandala, "A survey on MANET intrusion detection", International J. Computer Science and Security, volume 2, number 1, pages 1-11, 2008.
[3] Y. Zhang, W. Lee, and Y.A. Huang, "Intrusion detection techniques for mobile wireless networks", J. Wireless Networks, vol. 9, num. 5, pp.545-556, 2003.
[4] T.S. Sobh, "Wired and wireless intrusion detection system: Classifications, good characteristics and state-of-the-art", Elsevier J. Computer Standards and Interfaces, volume 28, number 6, pages 670-694, 2006.
[5] T. Anantvalee and J. Wu, "A survey on intrusion detection in mobile ad hoc networks", Springer J. Wireless Network Security, pages 159-180, 2007

Abstract: Nowadays, with portable computation equipment becoming extra commonly depended besides incorporated through extra sensitive structures such as infrastructure besides armed or regulation implementation. However, is being clearer when considering that malware and other types with cyberattacks attack such portable structures. Malware has always been an issue regarding any technological advances in the world of software. Smartphones and other mobile devices are thus to be expected to face the same problems. In this paper, mobile malware detection techniques presented in details. Furthermore, we have stated the recent techniques of mobile malware detection. Adding to that, these closest works to the paper objective are compared with respect to the based-portal, depended-approach, and significant system's description points.

Key Word: malware, cyberattacks, android malware, malware detection, mobile application

[1] D. B. Kirk and W. H. Wen-mei, Programming massively parallel processors: a hands-on approach. Newnes. Oxford, 2012.
[2] M. B. Abdulrazaq and O. M. Mustafa, "Designing and Implementing of An Online Library Managment System," Science Journal of University of Zakho, vol. 5, no. 3, Art. no. 3, Sep. 2017, doi: 10.25271/2017.5.3.396.
[3] S. Q. Sabri, A. M. Ahmad, and M. B. Abdulrazaq, "Design and Implementation of Student and Alumni Web Portal," Science Journal of University of Zakho, vol. 5, no. 3, pp. 272–277, 2017.
[4] A. A. Salih and M. B. Abdulrazaq, "Combining best features selection using three classifiers in intrusion detection system," in 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 94–99.
[5] H. I. Dino and M. B. Abdulrazzaq, "Facial Expression Classification Based on SVM, KNN and MLP Classifiers," in 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 70–75.

Abstract: Coronary Artery Disease (CAD) destroys the internal layer of the artery. Consequently, this destruction leads the fatty sediments to escalate the injury. CAD is one of the common significant reasons of death all around the world, thus early detection of CAD will facilitate scale back these rates. The medical industries gather a large number of facts which include some unknown data to make the choice effective. They also use some excellent data processing methods. The CAD prediction indicates the probability of patients getting artery disease. In this research, we propose various Machine Learning (ML) methods to predict the CAD with the help of historical data. These ML methods enable the system....

Key Word: Coronary Artery Disease (CAD;, KEEL (Knowledge Extraction based on Evolutionary Learning);Machine Learning(ML); Accuracy; Precision; Recall

[1]. Yufa Xia, Huailing Zhang, Lin Xu, Zhifan Gao, Heye Zhang, Huafeng Liu, Shuo Li ―An Automatic Cardiac Arrhythmia Classification System with Wearable Electrocardiogram‖, Institute of Electrical and Electronics Engineers(IEEE) Access, pp.51-56, vol. 6, 2018.
[2]. Liaqat Ali et al., ―An Automated Diagnostic System for Artery Disease Prediction Based on χ2 Statistical Model and Optimally Configured Deep Neural Network‖, IEEE Access, vol. 4, 2019.
[3]. Senthilkumar Mohan, Chandrasegar Thirumalai, and Gautam Srivastava ―Effective Artery Disease Prediction Using Hybrid Machine Learning Techniques‖, IEEE Access, 2019.
[4]. Afef Mdhaffar, Ismael Bouassida Rodriguez, Khalil Charfi, Leila Abid, and Bernd Freisleben ―CEP4HFP: Complex Event Processing for Artery Failure Prediction‖, IEEE Transactions on NanoBioscience, vol.16, no.8, pp.708-717, 21017

Abstract:: Internet users use the web every day for Browsing, Email, Banking, Social Media, and Web File & Video downloads. This research paper aims to help world internet users how not to get victimized to Emotet Malware - A Banking Credentials Stealer. Emotet is a very advanced modular trojan malware that primarily targets financial systems and internet users to steal financial and personal information by sending phishing emails to the people in question and self-spreading. Emotet also drops and downloads other banking trojans such as Trickbot, Ursnif, and IceDiD to exploit systems further and encrypts the large chunk of victim sensitive data with Ryuk ransomware payloads to benefit cyber attackers. United States Computer Emergency Readiness Team (US-CERT) issues an alert already concerning malicious Emotet campaign attackers. US-CERT also concluded that Emotet malware is the most destructive and costly malware affecting federal, state, local, tribal governments, private businesses, non-profit organizations, and individuals. A research conducted by top cybersecurity company CrowdStrike revealed that.....

Key Word: Emotet Malware; Geodo;Phishing; Banking Trojan; Credentials Theft; Malicious Payloads; Trickbot; Ursnif; IceDiD; Security SituationalAwareness (SSA); C2C;DLLs; Registry Keys; Spam;Ryuk Ransomware.

[1]. Niu, W., Li, T., Zhang, X., Hu, T., Jiang, T., & Wu, H. (2019). Using XGBoost to Discover Infected Hosts Based on HTTP Traffic. Security and Communication Networks, 2019.
[2]. Ceschin, F., Botacin, M., Gomes, H. M., Oliveira, L. S., &Grégio, A. (2019, November). Shallow security: On the creation of adversarial variants to evade machine learning-based malware detectors. In Proceedings of the 3rd Reversing and Offensive-oriented Trends Symposium (pp. 1-9).
[3]. Bhardwaj, A., & Goundar, S. (2019). A framework for effective threat hunting. Network Security, 2019(6), 15-19. doi:10.1016/s1353 4858(19)30074-1.
[4]. Azab, A., Layton, R., Alazab, M., & Oliver, J. (2014). Mining malware to detect variants. 2014 Fifth Cybercrime and Trustworthy
Computing Conference. doi:10.1109/ctc.2014.11.
[5]. Threat Advisory. (2019). The evolution of Emotet: From banking Trojan to threat distributor.
https://www.symantec.com/blogs/threat-intelligence/evolution-emotet-trojan-distributor

Abstract:: Sarcasm is the way of expressing opposite of what exactly is given. Generally, people use sarcasm to criticize. Sarcasm is merely a synonym of irony. Sarcasm is prevalent everywhere including social media. Twitter is a micro-blogging platform extensively used by people to express thoughts, reviews. It is a new trend to post sarcastic statements in order to avoid direct negativity. Hence it is necessary to implement an automated system that will be capable of understanding whether a statement is sarcastic or not. Already there are some systems that are implemented to meet this goal. The aim here is to develop a simpler model for finding the sarcasm in statements using NLP and an algorithm which produces results of good accuracy.

Key Word: Machine Learning, Natural Language Processing, Preprocessing, Sarcasm Detection, Sentiment Analysis, Support Vector Machine

[1]. Anukarsh G Prasad, Sanjana S, Skanda M Bhat, B S Harish "Sentiment Analysis for Sarcasm Detection on Streaming Short Text Data", 2nd International Conference on Knowledge Engineering and Applications, IEEE, 2017.
[2]. Sana Parveen, Sachin N. Deshmukh, "Opinion Mining in Twitter – Sarcasm Detection" International Research Journal of Engineering and Technology (IRJET), volume 04, issue 10, pages 201-204, October 2017.
[3]. Paras Dharwal, Tanupriya Choudary, Rajat Mittal, Praveen Kumar, "Automatic Sarcasm Detection using Feature Selection", International Conference on Applied and Theoretical Computing and Communication Technology, IEEE, 2017.
[4]. Sindhu. C, G. Vaidhu, Mandala Vishal Rao, "A Comprehensive Study on Sarcasm Detection Techniques in Sentiment Analysis", International Journal of Pure and Applied Mathematics, volume 118, pages 433-442, 2018.
[5]. Tanya Jain, NileshAgrawal, GarimaGoyal, Niyati Aggrawal, "Sarcasm Detection of Tweets: A Comparative Study", Tenth International Conference on Contemporary Computing (IC3), IEEE, August 2017.

Abstract:: This work as complete system is recommended not only for Police Commands in general but also for road safety commission in Nigeria and the model can be implemented elsewhere in the world. The former systemsin the previous design uses hardware machines and programmable interface to detect vehicle numbers. The existing works are much more of hardware system and the platforms are much more expensive and consumes space. They also require hidden device installations. This work will achieve a faster detection and location system of a stolen car. The car details are very logical in nature. The system uses a one-way authentication method toward accessing the system. This is achieved by generating a unique telemedicine code which....

Key Word: Vehicle detection, Car Recovery, Stolen Vehicle, Car theft.

[1]. Christos Nikolaos E. VassiliLoumos ,Anagnostopoulos, Ioannis E. Anagnostopoulos, ,and EleftheriosKayafas- A License Plate Recognition. Algorithm for Intelligent Trans-portation System Applications IEEE transaction on Intelligent Transportation System, Vol. 8, No. 3, Sept 2006.
[2]. Adger, Katherine. (2007) "An Analysis of Location and Offender Characteristics for Motor Vehicle Theft in Texas from 2001 to 2005." Master‟s thesis, University of North Texas, 2007.
[3]. Bender, Bryan. (2005) "Stolen U.S. Vehicles End up as Bombs in Iraq, FBI Says." NewYorkTimes, July16, 2007. tp://www.nytimes.com/2007/07/16/world/americas/16iht- 16cars.6683928.html?pagewanted=all. "US Car Theft Rings Probed for Ties to Iraq Bombings." The Boston Globe, October 2, 2005.
http://archive.boston.com/news/world/articles/2005/10/02/us_car_theft_rings_probed_for_ties_to_iraq_bombings/.
[4]. Block, Robert. (2006) "An L.A. Police Bust Shows New Tactics for Fighting Terror. Officers Use Local Laws to Arrest Small Offenders with High-Risk Potential." Wall Street Journal, December 29, 2006. https://www.wsj.com/articles/
SB116736247579862262.
[5]. Block, Steven. (2012) An Analysis of Internationally Exported Vehicle Thefts in Two High-Risk Cities. Newark, NJ: Rutgers University, 2012.

Abstract:: Ansible is a tool for configuration management. It's provided by Python internally & comes with Python 3. Hence for Ansible installation we need to use pip3 python package manager. pip3 install Ansible is the command to install Ansible. We can verify installation using Ansible — version command. This should be the latest Ansible version i.e. 2.9.11....

....

Abstract:: The enrollment of the primary government school always possess less compare to the private inspite of the facility and availabilty of the school. The paper studies all the possible factor which affects the enrollment of the student in the government school. The paper put forward the maximum factor in the form of attributes to analysis the underlying condition of the primary school in jorhat district. The analysis carried the data mining tool and techniques used in WEKA for algorithmic accuracy and study. The classification techniques are accepted for analysis of the attributes and conditions. Cross fold condition and slip value condition is also checked for data consistency and accuracy.

Key Word: Primary Government School; Enrollment; Data Mining Techniques; WEKA; Algorithms.

[1]. B. K. Baradwaj and S. Pal, "Mining educational data to analyze students' performance "International Journal of Advanced Computer Science and Applications, vol. 2, 2011.
[2]. A. Nandeshwar and S. Chaudhari. (2009). Enrollment Prediction Models Using Data Mining. [Online]. Available: http://nandeshwar.info/wp-content/uploads/2008/11/DMWVU_Project.pdf
[3]. D. Kabakchieva, "Predicting student performance by using datamining methods for classification," Cybernetics and InformationTechnologies, vol. 13, 2013.
[4]. Z. J. Kovačić, "Early prediction of student success: Mining students enrolment data," presented at the Informing Science & IT Education Conference, 2010. ques," (Morgan-Kaufman Series of Data Management Systems). San Diego: Academic Press, 2001.

Abstract:: Being the exchange where the issuing and trading of equities or stocks of publicly held companies take place, the stock market is one of the most vital components of a market's economy. Stock market prediction is the process of attempting to predict the future values of a company based on its previous data to enhance the probability of a successful trade for an investor. In a financially volatile stock market, it is important to have a very precise prediction of future trends. Stock prediction involves the prediction in advance on whether the future market will close higher or lower compared to its opening levels. The stock market data is highly noisy, irregular and chaotic in nature. Hence proven to be a daunting task for market researchers and investors to make buy or sell decisions. A number of techniques....

[1]. M. Piper, "obliviousinvestor.com," 2009. .
[2]. A. Zheng and J. Jin, "Using AI to Make Predictions on Stock Market," pp. 1–6.
[3]. "sigmoidal.io," LLC, Sigmoidal. .
[4]. N. Singh Pahwa and N. Khalfay, "Stock Prediction using Machine Learning a Review Paper," Int. J. Comput. Appl., vol. 163, no. 5, pp. 975–8887, 2017.
[5]. L. Auria and R. a Moro, "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discuss. Pap. Dtsch. Inst. Wirtschaftsforsch., no. August, 2008.

Abstract:: The conventional technique of measuring the quality of water is to gather the samples manually and send it laboratory for analysis, but this technique is time overwhelming and not economical. Since it's not feasible to take the water sample to the laboratory after every hour for measuring it's quality. The water quality measuring system can measure the essential qualities of water in real time. The system consists of multiple sensors to measure the standard of water, microcontroller and Cloud dashboard display to show the information about the parameters to the end users. It's a true time system which is able to endlessly measure the standard of water and can send the measured values to the Cloud when each predefined time. The system relies on microcontroller ESP8266.An individual easy to know the quality of water at any point of time at any place and at the any time without putting any money and in an easy and in an efficient manner.

Keywords: Microcontroller, Multiple sensors, Water Quality Measuring, Cloud Dashboard Display

[1]. Mo Deqing, Zhao Ying, Chen Shangsong, "Automatic Measurement and Reporting System of Water Quality Based on .....
[5]. © 2008 IEEE

Abstract: Image change detection is a technique that evaluates images of the similar scene obtained at different times to classify the changes that may have happened among the multitemporal images.It has been found that the existing technique suffer from various issues such as poor visibility, complex background images, etc. Also, the existing techniques are computationally poor, that means take more time to evaluate the change in remote sensing images. It is also found that the existing techniques perform poor when objects are inherently like each other.Therefore, in this research work, a novel remote sensing image change detection technique is designed and implemented. Initially, we have considered the pre-processing of images to enhance the visibility of these images and to resize these images. Thereafter, a k-nearest neighbor algorithm is used to segment the remote sensing image. Finally, change detected in remote sensing images are classified. The proposed and competitive remote sensing image change detection.......

Keywords: Change detection, Remote sensing images, Image fusion, Optimizaiotn

[1]. Celik, T. (2010), "A Bayesian approach to unsupervised multiscalechange detection in synthetic aperture radar images‟, Signal Processing,90(5):1471–1485.
[2]. Heng-Chao Li, Celik, T., Longbotham, N., & Emery, W. J. (2015), "GaborFeature Based Unsupervised Change Detection of Multitemporal SAR ImagesBased on Two-Level Clustering‟, IEEE Geoscience and Remote SensingLetters, 12(12):2458–2462.
[3]. Zanetti, M., Bovolo, F., & Bruzzone, L. (2015), "Rayleigh-Rice MixtureParameter Estimation via EM Algorithm for Change Detection in MultispectralImages‟, IEEE Transactions on Image Processing, 24(12):5004–5016.
[4]. Vicinanza, M. R., R. Restaino, G. Vivone, M. Dalla Mura and J. Chanussot (2015). APansharpening Method Based on the Sparse Representation of Injected Details. IEEETransactions on Geoscience and Remote Sensing Letters, Vol. 12, No. 1, pp. 180-184.
[5]. Mamta Juneja ,Ekta Walia, Parvinder Singh Sandhu, and Rajni Mohana , Implementation and Comparative Analysis of Rough Set, Artificial Neural Network (ANN) and Fuzzy Rough classifiers for Satellite Image Classification, IEEE International Conference on Intelligent Agent & Multi – Agent Systems,IAMA,pp.l-6,2009.

Abstract:: Public key cryptography is now a vital element of our global communication digital infrastructure.Most of our important communication protocols depend on primary cryptographic functionalities for secure communicationThe invention of quantum machines are likely to be applied to unravel certain challenges when compared with classical computer systems, which has stimulated good curiosity about quantum computing.Quantum computer systems are now being developed are capable of breaking much of the current public key system, undermining the privacy and protection of digital internet communications and post-quantum encryption, aiming at building......

Keywords: Java Cards, Quantum Computer, Public key cryptography , Post Quantum Cryptography

[1]. Johannes, Buchmann ,Denis Butin, Florian G¨opfert, and Albrecht Petzoldt,"Post-Quantum Cryptography: State of the Art", Springer Heidelberg LNCS 9100, pp. 88–108, (2016)
[2]. Songyan Liu,Zhigang Mao&Yizheng Ye "Implementation of Java card Virtual Machine"Journal of Computer Science and Technology volume 15, pages591–596 2000
[3]. ShardhaPorwal,HimanshuMittal"An Efficient Memory Management Technique For Smart Card Operating System" International Journal OF Computers & Technology 5(2):124-129 · June 2006
[4]. Ajtai, M.: "Generating hard instances of lattice problems" Annual ACM Symposium on Theory of Computing, STOC 1996, pp. 99–108. (1996)
[5]. Daniel J, bernstein1 , Tanja Lange : "Post-quantum cryptography" ,Springer Nature VOL 549, September (2017)

Abstract: As we know the needs to apply the meaning of business continuity of data center in any organization, this paper introduces a newly prototype model for monitoring system based on Internet of Things (IOT). The idea is to design a solution consist of the hardware connected with set of sensors. These devices connected to main monitoring system by Ethernet or Global System for Mobile (GSM).Thisarticle targets securing the data centers from environmental turbulences. By enterprise solution with multi Internet of Things (IoT), the system will send the environmental records to a centralized system which will analyze the data and recommend or take necessary actions. The centralized system will have a portal and contains real time data and historical saved records. These data will be displayed in a graphical dashboard with live charts. The system contains levels of several alerting functions such as (onsite buzzers, portal colored alerts, SMS, emails and customizable alerts based on the admin needs). The portal will be hosted on cloud premises..

Keywords: Internet of Things, Data centers, cloud computing, sensors

[1]. Lionel Nkenyereye1 , Jongwook Jang1 , " Design of Data Center Environmental Monitoring System Based On Lower Hardware Cost " , Journal of Multimedia and Information System ISSN2383-7632(Online), VOL. 3, NO. 3, (pp. 63-68) September 2016.
[2]. Ferdin Joe , John Joseph , "IoT Based Weather Monitoring System for Effective Analytics", International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Issue-4, April, 2019 .
[3]. Seelam Vasavi Sai Viswanada Prabhu Deva Kumar, Shyam Akashe, "Implementation of GSM Based Security System with IOT Applications" , I. J. Computer Network and Information Security, 2017, 6, 13-20 Published Online June 2017.
[4]. Sneha S. Mane , Girish R. Talmale, "Raspberry Pi Based Security System on IoT Platform", International Conference on Recent Trends in Engineering Science and Technology (ICRTEST 2017) ISSN: 2321-8169 Volume: 5 Issue: 1 Special Issue 21-22 January 2017.
[5]. Lionel Nkenyereye , Jong-wook Janga , " A Remote System for Monitoring Auxiliary Data Center from Environmental threats with Lower Hardware Cost" , The 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2016) , Procedia Computer Science 187 – 192- 98 ( 2016 ).

Abstract:: This research explore on studying road extraction from satellite images, based on deeplearning for semantic segmentation. Previousapproaches for extraction of roadsfromsatelliteimageryisusingmanualapproach, but itis time taking and tedious. The otherapproachisusingsemi-automatic techniques for road extraction from high resolutionsatelliteimagery. However, thisresearchfocuses on fullyautomatic road extraction based on deeplearning. Fullyconvolutional neural network isused to extractroadsfrom the satellite images. For training the CNN model,hugeamount of labeled and structureddata isneeded, but thereis no suchabigdataset for road extraction thatispubliclyavailable......

Keywords: Road Extraction, Data Augmentation, FullyConvolutional Neural Network, Deconvolution, and ConditionalRandom Field.

[1]. RashaAlshehhi, Prashanth Reddy Marpu, Wei Lee Woon, Mauro Dalla Mura, "Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks" ISPRS Journal of Photogrammetry and Remote Sensing 130 (2017) 139–149.
[2]. Ruyi Liu, Qiguang Miao, Jianfeng Song, YiningQuan, Yunan Li, PengfeiXu, Jing Dai "Multiscale road centerlines extraction from high-resolution aerial imagery", Neurocomputing 329 (2019) 384–396.
[3]. Yanan Wei, Zulin Wang, and Mai Xu, "Road Structure Refined CNN for Road Extraction in Aerial Image", IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 14, NO. 5, MAY 2017, 709-713.
[4]. Campbell, J. B. 2002. Introduction to Remote Sensing. New York London: The Guilford Press.
[5]. Weixing Wang, Nan Yang, Yi Zhang, Fengping Wang, Ting Cao, PatrikEklund "A review of road extraction from remote sensing images", ScienceDirect (journal of traffic and transportation engineering), 2016