The Internet as a fast-growing communication infrastructure comes with additional challenges of cybersecurity. A few techniques have been created to provide security in the application, transport, or network layer of a network. Many organizations have worked to provide security at higher OSI layers, from application layer right down to the network layers, without any at the Data Link layer. This has opened up many systems to a variety of compromises and attacks. This study proposes the provision of the public key Elliptic Curve Cryptography to serve the Data Link Layer instead of the Media Access Control (MAC). In this study, visible light communication technology for fast data communication and secure data transmission on the data link layer using public-key cryptosystem are discussed. The visible light communication technology consists of Light Emitting Diodes (LED) that flicker at an incredibly high frequency, thereby enabling a very high-speed wireless communication and an elliptic curve encryption component that carries out an integrated encryption scheme using Elliptic Curve Integrated Encryption Scheme (ECIES) and a digital signature algorithm using Elliptic Curve Digital Signature Algorithm (ECDSA). For the data link layer security, the encryption procedure is applied to the communications server and the programmable circuit boards (PCB) controlling the visible light communication devices. While the complete system model was implemented in a program, a prototype of the architecture was implemented on a microFourQ-MSP-IAR Embedded Workbench IDE-MSP430 7.12.4, and Wolfram Mathematica. Two advantages of visible light communication and public-key encryption were demonstrated: 1) providing security for the data link layer messages. 2) using VLC to speed up the encryption and decryption process in the data link layer.

Internet of Things, Simulated Systems, Visible Light Communications, Elliptic Curve Cryptography, VLC, ECC, IoT, OSI, Data Link Layer, PKI

[1]  G.
Bora, S. Bora, S. Singh, & S.M. Arsalan, OSI reference model: An
overview. International Journal of Computer Trends and Technology
(IJCTT), Vol 7 Issue 4, pp.214-218, 2014.

[2]  Y.
Li, D. Li, W. Cui, and R. Zhang, "Research based on OSI model." In
the proceedings of the 2011 IEEE 3rd International Conference on Communication
Software and Networks (3ICCSN - 2011), Xi`an, China, pp.554-557,
2011.

[3]  N.
Briscoe, "Understanding the OSI 7-layer model." PC Network
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pp.13-16, 2000.

[4]  J.
Franklin, D. McCoy, P. Tabriz, V. Neagoe, J.V. Randwyk, and D. Sicker,  Passive Data Link Layer 802.11 Wireless
Device Driver Fingerprinting. In USENIX Security Symposium Vol. 3, pp. 16-89, 2006.

[5]  S.
M. AlMheiri and H. S. AlQamzi, "Data link layer security protocols in
Wireless Sensor Networks: A survey," 2013 10th Ieee International
Conference on Networking, Sensing and Control (ICNSC - 2013), pp.312-317, 2013.

[6]  M.
Meribout and A. Al Naamany, "A collision-free data link layer protocol for
wireless sensor networks and its application in intelligent transportation
systems," 2009 Wireless Telecommunications Symposium, Prague, pp.1-6,
2009.

[7]  D.
Altolini, V. Lakkundi, N. Bui, C. Tapparello and M. Rossi, "Low power link
layer security for IoT: Implementation and performance analysis," 2013
9th International Wireless Communications and Mobile Computing Conference
(IWCMC), Sardinia, pp.919-925 2013.

[8]  A.
Annapurna, S. Madhuri, & D. Mohammed. Data Link Layer Security Issues. International
Journal of Computer Science & Engineering Technology, Vol 4 Issue 7, pp.1009-1102, 2013.

[9]  B.
Alotaibi, & K. Elleithy. A New MAC Address Spoofing
Detection Technique Based on Random Forests, Sensors, Vol
16,
Issue 681,
pp.1-14, 2016.

[10] T. OConnor, Detecting and Responding to Data Link Layer
Attacks. Boston: SANS Institute 2019.

[11]K. Singh, Security Issues in Wireless Networks. Research
Gate, pp. 1-5, 2014.

[12]K. Tao, J. Li, & S. Sampalli, Detection of Spoofed MAC
Addresses in 802.11 Wireless Networks. Springer, Vol 1, Issue 23, pp.201-213, 2008.

[13]L. Wong, An Overview of 802.11 Wireless Network Security
Standards; Mechanisms. Swansea: SANS Institute, 2005.

[14] H. Haas, Li-Fi - "Shedding Light on Future Wireless
Communications." 2011 TED Conference, Edinburgh. 2015.

[15]J. Patel, F. Suthar, & S..V.O. Khanna, "A Critical
Analysis on Encryption Techniques used for Data Security in Cloud Computing and
IoT (Internet of Things) based Smart cloud storage System: A Survey,"
International Journal of Scientific Research in Network Security and
Communication, Vol.7, Issue.2, pp.21-25, 2019.

[16]E.I. Davies & V.I.E. Anireh, Design and Implementation of
Smart Home System Using Internet of Things. Journal of Digital Innovations
& Contemp Res. In Sc., Eng & Tech, Vol
7, Issue 1, pp.22-42, 2019.

[17]A. Oyegoke, The constructive research approach in project
management research. International Journal of Managing Projects in Business, Vol 8, Issue 5/6, pp. 573–595, 2011.

[18] G. Crnkovic, Constructive Research and Info-computational
Knowledge Generation. Springer, Vol 314,
Issue 1, pp. 359–380, 2010.

[19]M. Morales-Sandoval, Hardware architecture for elliptic curve
cryptography and lossless data compression. Puebla: Computer Science
Department National Institute for Astrophysics, Optics and Electronics. pp. 1-89, 2004.

[20]R.Piplode,
P. Sharma and U.K. Singh, "Study of Threats, Risk and Challenges in Cloud
Computing," International Journal of Scientific Research in Computer
Science and Engineering, Vol.1,
Issue.1, pp.26-30, 2013.

D. Ene, V.I.E. Anireh, D. Matthias, "Data Link Layer Encryption for The Internet of Things Using Elliptic Curve Cryptography Over Visible Light Communication Channel," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.52-58, 2020.

Image Steganography is the mechanism of hiding the critical information on the segment of the image that can either be least significant or most significant in nature. Image Steganography involves multiple images that are merged together to achieve a common image that is transferred over a digital medium. Proposed system uses slant let transformation to achieve better result of image security in terms of accuracy. Accuracy is achieved by minimising mean square error and improving peak signal to noise ratio. 

Image Steganography, Slant let Transformation. PSNR, MSE

[1]   C. Science
and S. Engineering, “Steganography Digital Images : A Hybrid Approach,” vol. 5, no. 5, pp. 1778–1785, 2015.

[2]   P. Parmar and N.
Jindal, “Image Security with Integrated Steganography and Encryption 1 1 2,” vol. 9, no. 3, pp. 24–29, 2014.

[3]   T. Bathinda,
“Invisible Video Multiple Steganography Using Optimized Techniques,” 2016.

[4]   R. T. Mohammed and
B. E. Khoo, “Image Steganography using slantlet transform,” ISIEA 2012 -
2012 IEEE Symp. Ind. Electron. Appl., pp. 281–286, 2012.

[5]   R. K. Sheth and V.
V. Nath, “Secured digital image Steganography with discrete cosine transform
and discrete wavelet transform method,” 2016 Int. Conf. Adv. Comput. Commun.
Autom., pp. 1–5, 2016.

[6]   R. V Mahule,
“Analysis of Image Security Techniques using Digital Image Steganography in
Spatial Domain,” no. Nckite, pp. 19–26,
2015.

[7]   Z. J. Xu, Z. Z.
Wang, and Q. Lu, “Research on Image Steganography Algorithm based on DCT,” vol. 10, pp. 1129–1135, 2011.

[8]   A. U. Islam, F.
Khalid, M. Shah, Z. Khan, T. Mahmood, A. Khan, U. Ali, and M. Naeem, “An
improved image steganography technique based on MSB using bit differencing,” 2016
6th Int. Conf. Innov. Comput. Technol. INTECH 2016, pp. 265–269, 2017.

[9]   V. Saravanan and
A. Neeraja, “Security issues in computer networks and stegnography,” 7th
Int. Conf. Intell. Syst. Control. ISCO 2013, pp. 363–366, 2013.

[10] P. Singhai and A.
Shrivastava, “An efficient Image Security mechanism based on Advanced
Encryption Standard,” no. 13, 2015.

[11] S. S. Gonge, “An
Integration of SVD Digital Image Steganography with AES Technique for Copyright
Protection and Security of Bank Cheque Image,” pp. 769–778, 2016.

[12] Q. Chen, H. Hu, and
J. Xu, “Authenticated Online Data Integration Services,” pp. 167–181.

[13] J. Singh and A. K.
Patel, “An Effective Telemedicine Security Using Wavelet Based Steganography,” pp. 2–7, 2016.

[14] M. Rizal, M. Isa,
and S. Aljareh, “A Steganography technique to improve the security level in
face recognition systems,” Multimed. Tools Appl., 2016.

Divya Soi, Bhupinder, "A Robust Hybrid Algorithm for Image Steganography," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.59-68, 2020.

Water leakages in underground distribution of water through pipelines are the major issue faced by all. The inspections made by Human resources to find leakage are difficult in the real time. In this project, we propose a leak detection system to continuously monitor the underground water pipelines to make the man work easier. This system detects the leak as soon as any damages made to the pipelines due to several factors such as the pipe’s age, improper installation, and natural disasters. Here the water management staff is informed about the leakages through message. The staff will be able to determine the leak of water and its precise location of the leakage. The system is made up of basic components: sensors, GSM module, Raspberry-pi. Mobile phone’s message and the mail is the alert transmitter of the system to the user and higher authorities in case of water leaks. This project helps the water department workers to identify the underground water leakage in an effective way and reduces the wastage of water which is unnoticed by humans.

Global System for Mobile Communication(GSM), Hall Effect Sensor, Raspberry-pi, Leakage Detection

[1]Mohamed Fahmy and
Osama Moselhi," Detecting and
Locating leaks in Underground Water Mains Using Thermography",
International Symposium on Automation and robotics in construction,pp.61-67,
2009.

[2]Ahmed Imteaj,Tanveer Rahman, Muhammad Kamrul Hossain and
Saika Zaman," IoT based Autonomous
Percipient Irrigation System using Raspberry Pi",1^9th International Conference on
Computer and Information
Technology,pp. 563-568, 2016.

[3]Sanam Pudasaini, Anuj Pathak, Sukirti Dhakal, Milan
Paudel," Automatic
Water Level Controller with Short Messaging Service (SMS)
Notification",International Journal of Scientific and Research
Publications, ISSN 2250-3153,pp.1-4, 2014.

[4]Arjun K, Dr. Latha C A, Prithviraj," Detection of
Water Level, Quality and Leakage using Raspberry Pi with Internet of
Things", International
Research Journal of Engineering and Technology(IRJET), p-ISSN: 2395-0072,pp.2875-2880, 2017.              

[5]Saraswathi V, Rohit A, Sakthivel S, Sandheep T
J,"Water Leakage System Using IoT", International Journal of Innovative Research in
Engineering & Management (IJIREM), ISSN: 2350-0557,pp.120-123,
2018.

[6]Maroua Abdelhafidh, Mohamed Fourati, Lamia Chaari Fourati,
and Amor Abidi," Remote Water Pipeline Monitoring System IOT-based
Architecture for new Industrial era 4.0", 14th International Conference on Computer Systems and
Applications,pp.1184-1191, 2017.

[7]Alexandru
Predescu, Mariana Mocanu, Ciprian Lupu,"A modern approach for leak detection in water distribution systems", International Conference on System Theory, Control and
Computing (ICSTCC),pp.486-491, 2018.

[8]P.Swetha Reddy, K.V.Chanakya, B.Eswari,Ch.Bhupati,"Water leakage detection monitoring and controlling system using IOT", International Journal of Engineering & Technology,pp.120-123,
2018.

[9]Kiat Siong
N,Pei-Yin Chen, Yuan-Chi Tseng," A
Design
of Automatic Water Leak Detection Device", International Conference on Opto-Electronic Information
Processing,pp.71-73, 2017.

[10]Pranita Vijaykumar Kulkarni, Mrs. M.
S. Joshi," An IOT based Water Supply Monitoring and Controlling System
with Theft Identification", International
Journal of Innovative Research in Science, Engineering and Technology,
pp.16152-16157, 2016.

[11]L.A. Gama-Moreno, A. Corralejo, A. Ramirez-Molina,J. A.
Torres- Rangel, C. Martinez-Hernandez, M.A. Juarez ," A Design of a
Water Tanks Monitoring System Based on Mobile Devices",International Conference on Mechatronics, Electronics and Automotive Engineering,pp.133-138, 2016.

[12]Theofanis P.
Lambrou, Christos C. Anastasiou, Christos G. Panayiotou, and Marios M.
Polycarpou," A Low-Cost
Sensor Network for Real-Time Monitoring and Contamination Detection in Drinking
Water Distribution Systems, IEEE SENSOR JOURNAL,pp.2765-2772, 2014.

[13]Varsha
Radhakrishnan,Wenyan Wu,"IOT technology for Smart Water System”,InternationalConference
on High Performance Computing and Communications,pp.1493 to 1498,
2018.

[14]Swetha
Reddy P, Chanakya K.V ,Eswari B, Bhupathi Ch,"Water leakage detection
monitoring and controlling system using IOT”,International Journal of Engineering and
Technology,pp.120 to 123, 2018.

[15]Cheng W.P, Yu
T.C,Xu G,"Real-Time Model of a
Large-Scale Distribution System",16^th Conference on Water
Distribution System Analysis, Science
Direct,pp.457 to 466, 2014.

G. Sindhu, T. Sathya Shanmathi, A.V. Menakadevi, K. Srinivasan, "Enhanced Leak Detection in Water Distribution System Using IoT," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.69-73, 2020.

Road Divider is used for dividing the road for ongoing and incoming traffic. This helps keeping the flow of traffic in control. The problem with Static Road Dividers is that the number of lanes on either side of the road is fixed. This calls for better utilization of existing resources like number of lanes available.The main aim is to formulate a mechanism of automated road divider that can shift lanes at one side, so other side can have number of lanes in the direction of the rush. The cumulative impact of the time and fuel that can be saved by adding even one extra lane to the direction of the rush will be significant.With the smarter application proposed below,it will be helpful eliminate the dependency on manual intervention and manual traffic coordination to have a smarter traffic all over the city. An Automated road divider can provide a solution to the above mentioned problem effectively.

Automated road divider, traffic system, arduino board

[1] B DurgaSri, K
Nirosha, Sheik Gouse “Design and implementation of
smart movable road divider using IOT”, Published in: 2017 International Conference on Intelligent
Sustainable Systems (ICISS), December
2017.

[2] George
Kiokes, “Development of an Integrated Wireless Communication System for
Connecting Electric Vehicles to the Power Grid”, IEEE conf., pp 296-301, 2015.

[3] Hemlata Dalmia,Kareddy Damini, Aravind Goud Nakka “Implementation
of Movable Road Divider using Internet of Things (IOT)”, Published in: International Conference on Computing, Power and
Communication Technologies (GUCON),
MARCH 2019.

[4] Jyothirmayee
,G Vamshi Krishna, J Nanditha, B Shashank Yadav “Controlling Of traffic using
Movable Road Dividers” , Published in: International Journal of Advance
Engineering and Research Development(IJAERD), APRIL 2018.

[5] K.Vidhya,
A.Bazila Banu, Density Based Traffic Signal System", Volume 3, Special Issue 3, March 2014

[6] N Naveen, C N
Sowmya (February 2019)” IoT Deployed Automatic Movable Smart Road Divider to
Avoid Traffic Problems”, Published in: International Journal of Computer
Science Trends and Technology (IJCST)

[7] Pallavi
Choudekar, Ms.Sayanti Banerjee, Prof.M.K.Muju, Real Time Traffic Light Control
Using Image Processing" Vol. 2, No.
March.

[8] Rajeshwari
Sundar, Santhosh Hebbar, and Varaprasad Golla, Implementing intelligent Traffic
Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle
Detection" IEEE Sensors Journal, Vol.
15, No. 2, February 2015.

[9] S.Lokesh, “An
Adaptive Traffic Control System Using Arduino”, International journal of
engineering sciences & research Technology, IEEE conference, pp 831-835, June 2014.

Anireddy Sushrutha, C.R.K. Reddy, "Movable Road Divider Using Internet of Things," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.74-76, 2020.

Wireless Sensor Network (WSN) has increased the interest of researchers in several challenging characteristics. The most vital challenge in these networks is energy minimization. One of the most accepted solutions in constructing the energy-efficient WSN is to cluster the networks. In clustering, the nodes are split into some clusters and few nodes are elected to be cluster heads. In a clustered WSN, the normal nodes can transmit their sensed data to the cluster head. Then, the cluster head can aggregate and transmit those data to the base station. Typically, clustering involves different merits such as scalability, energy-efficiency and minimizing routing delay. In this article, a detailed comparative survey on energy-efficient clustering and routing protocols in WSN is presented. At first, energy-efficient clustering and routing protocols proposed for WSN in previous researches are studied in detail. After that, a comparative and state-of-the-art analysis is carried out to identify the limitations in those protocols and suggest a novel improvement on clustering and routing protocol with increased network lifetime and reduced energy consumption.    

WSN, Clustering, Routing protocol, Cluster head, Data aggregation

[1]     Aditya Singh Mandloi, Vinita
Choudhary, “An efficient clustering technique for deterministically deployed
wireless sensor networks”, International Journal of Scientific Research in
Network Security and Communication, Vol.1,
Issue.1, pp.6-10, 2013.

[2]     M. M. Afsar, M. H. Tayarani-N,
“Clustering in sensor networks: A literature survey”, Journal of Network and Computer Applications, Vol.46,
pp.198-226, 2014.

[3]     A. Garg, N. Batra, I. Taneja, A.
Bhatnagar, A. Yadav, S. Kumar, “Cluster formation based comparison of genetic
algorithm and particle swarm optimization algorithm in wireless sensor
network”, International Journal of Scientific Research in Computer Science and
Engineering, Vol.5, Issue.2, pp.14-20, 2017.

[4]     N. G. Palan, B. V. Barbadekar, S.
Patil, “Low energy adaptive clustering hierarchy (LEACH) protocol: A
retrospective analysis”, In 2017 IEEE
International Conference on Inventive Systems and Control (ICISC), pp.1-12, 2017.

[5]     F. Zhao, Y. Xu, R. Li, “Improved
LEACH routing communication protocol for a wireless sensor network”, International Journal of Distributed Sensor
Networks, Vol.8, Issue.12, pp.1-6, 2012.

[6]     T. Liu, Q. Li, P. Liang, “An
energy-balancing clustering approach for gradient-based routing in wireless
sensor networks”, Computer
Communications, Vol.35, Issue.17, pp.2150-2161, 2012.

[7]     H. Li, Y. Liu, W. Chen, W. Jia, B.
Li, J. Xiong, “COCA: Constructing optimal clustering architecture to maximize
sensor network lifetime”, Computer
Communications, Vol.36, Issue.3, pp.256-268, 2013.

[8]     N. Mittal, U. Singh, B. S. Sohi,
“A stable energy efficient clustering protocol for wireless sensor networks”, Wireless Networks, Vol.23,
Issue.6, pp.1809-1821, 2017.

[9]     W. Zhang, L. Li, G. Han, L. Zhang,
“E2HRC: an energy-efficient heterogeneous ring clustering routing protocol for
wireless sensor networks”, IEEE Access,
Vol.5, pp.1702-1713, 2017.

[10]  K. A. Darabkh, N. J. Al-Maaitah,
I. F. Jafar, K. Ala’F, “EA-CRP: A Novel Energy-aware Clustering and Routing
Protocol in Wireless Sensor Networks”, Computers
& Electrical Engineering, 2017.

[11]  K. A. Darabkh, S. Wala`a, M. Hawa,
R. Saifan, “MT-CHR: A modified threshold-based cluster head replacement
protocol for wireless sensor networks”, Computers
& Electrical Engineering, 2018.

[12]  K. A. Darabkh, S. Wala’a, R. T.
Al-Zubi, S. H. Alnabelsi, “C-DTB-CHR: centralized density-and threshold-based
cluster head replacement protocols for wireless sensor networks”, The Journal of Supercomputing, Vol.73,
Issue.12, pp.5332-5353, 2017.

[13]  K. A. Darabkh, S. M. Odetallah, Z.
Al-qudah, K. Ala’F, “A New Density-Based Relaying Protocol for Wireless Sensor
Networks”, In 2018 14th IEEE International
Wireless Communications & Mobile Computing Conference (IWCMC), pp.712-717, 2018.

[14]  K. A. Darabkh, M. Z. El-Yabroudi,
A. H. El-Mousa, “BPA-CRP: A balanced power-aware clustering and routing
protocol for wireless sensor networks”, Ad
Hoc Networks, Vol.82, pp.155-171, 2019.

M. Karthik, S. Mohanapriya, "Energy-Efficient Clustering and Routing Protocols in Wireless Sensor Network: A Survey," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.77-80, 2020.

Agile has emerged as the most popular approach to project management because of its iterative and flexible nature. It is described by a prominence on responding to changes. There are various prominent agile frameworks used by the industries and scrum is one of it. This paper focuses on the detailed overview of agile scrum framework. It elaborates the challenges / limitations in scrum implementation. The point of convergence of the paper is proposing the algorithm-based remediation for the story prioritization process of scrum framework.  The proposed framework also confronting the problem of scope creep faced by scrum squad. The targeted audience is professionals who are involved in scrum project management methodology.

Agile, Scrum, Scope-Creep, Stories, Product Owner, Backlog

[1]
The Scrum Guide: The Definitive Guide to Scrum: The Rules of the Game.

[2] Eliza S. F. Cardozo , J. Benito F. Araújo Neto , Alexandre Barza , A. César C. França , Fabio Q. B. da Silva, SCRUM and Productivity in Software
Projects: A Systematic Literature Review, 2010.

 [3] Daniel Haslinger & Dr. Andreas Wintersteiger,
White Paper: Implementing Scrum & Agile Development, 2017.

[4] Julian M. Bass,Scrum Master Activities:
Process Tailoring in Large Enterprise Projects IEEE, 2014.

[5] Dan Rawsthorne & Douglas
Shimp , White Paper: Scrum 3.0 –  3BACK,
LLC, 2017.

[6] Santosh Kumar Yadav, Naveen
Kumar Jangid, S Rajeshwari, A Survey on Agile Software Development
Methodologies, IJSRCSEIT, 2017.

[7] K M. Jyoti, Mr. Tinku Singh,
Parul Saharavat, Analysis the Strength of Agile Methodologies in Software
Development, IJSRCSEIT, 2018.

[8] Eliza S. F. Cardozo, J. Benito
F. Araújo Neto, Alexandre Barza, A. César C. França, Fabio Q. B. da Silva,
SCRUM and Productivity in Software Projects: A Systematic Literature Review

[9] KevinVlaanderen,
SlingerJansen,
SjaakBrinkkemper, The agile requirements
refinery: Applying SCRUM principles to software product management -

[10] Problems in the Adoption
of Agile-Scrum Methodologies: A Systematic Literature Review – IEEE, 2016.

[11] Shruti
Sharma , Nitasha
Hasteer, A comprehensive study on
state of Scrum development , 2016.

[12] DEEPA VIJAY,
GOPINATH GANAPATHY, Empirical Case Study of Agile Scrum
Process, 2013.

 [13]Astha Singhal, Divya Gupta, Scrum: An
Agile Method - International Journal of Engineering Technology, Management and
Applied Sciences, 2014.

[14] Youry Khmelevsky, Xitong Li, Stuart Madnick, Software development
using agile and scrum in distributed teams-IEEE, 2017

[15] Monika Agarwal, Prof. Rana
Majumdar- Tracking Scrum projects Tools, Metrics and Myths About Agile,
International Journal of Emerging Technology and Advanced Engineering, 2012.

[16]Nagy Ramadan Darwish, Salwa Megahed, Requirements
Engineering in Scrum Framework, 2016.

Darshita Kalyani, Devarshi Mehta, Priti Sajja, "Modified Architecture and Innovative Algorithm for Story Prioritization in Scrum Framework to Aid Product Owner," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.81-86, 2020.

In today’s scenario, there is an explosion of knowledge and the context of what is learnt and how it is learnt by the students become very important to understand. Student needs to develop their higher order thinking skills which would help them understand and apply concepts in the right manner. This research work specifically focuses on emotion detection of the students by analysing their facial expressions while they are answering a set of questions asked by an instructor for different subjects. The aim is to identify the cognitive learning level of the Bloom’s Taxonomy and map the emotions set to each learning level. This is achieved by capturing the emotions of students from an emotion classifier based on CNN (Convolutional Neural Networks) and further classifying them using various classification algorithms which in turn predicts the Accuracy of the proposed system.

Biometrics, Emotions, Cognitive learning level

[1]    
Bloom, B. S.; Engelhart,
M. D.; Furst, E. J.; Hill, W. H.; Krathwohl, D. R. (1956). Taxonomy
of educational objectives: The classification of educational goals. Handbook I:
Cognitive domain. New York: David McKay Company.

[2]    
Shane, Harold G. (1981).
"Significant writings that have influenced the curriculum: 1906-1981". Phi
Delta Kappan. 62 (5): 311–314.

[3]    
Krathwohl, David R. (2002).
"A revision of Bloom`s taxonomy: An overview". 

[4]    
Anderson, Lorin W.; Krathwohl, David R., eds.
(2001). A taxonomy for learning, teaching, and assessing: 5. BJ Jansen, D
Booth, B Smith (2009) Using
the taxonomy of cognitive learning to model online searching, Information Processing
& Management 45 (6), 643-663

[5]    
Anderson LW, Krathwohl
DR. A taxonomy for learning, teaching, and assessing: a revision of
Bloom’s taxonomy of educational objectives. New YorkNY: Longmans; 2001.

[6]    
Alison Cullinane, Bloom’s
Taxonomy and its Use in Classroom Assessment, National Centre for Excellence in
Mathematics and Science Teaching and Learning, Resource & Research Guides
Vol. 1 #13 2009-10

[7]    
Octavio Arriaga, Paul G. Pl¨oger,
Matias Valdenegro Real-time Convolutional Neural Networks for Emotion and
Gender Classification

[8]    
Monika Dubey, Prof. Lokesh
Singh, Automatic Emotion Recognition Using Facial Expression: A Review, IRJET,
Feb 2016

[9]    
Byoung Chul Ko, A Brief Review
of Facial Emotion Recognition Based on Visual Information, Jan 2018

[10] Octavio Arriaga, Paul G. Pl¨oger, Matias Valdenegro Real-time Convolutional
Neural Networks for Emotion and Gender Classification, ICRA 2018

[11] An empirical study on assessment of co attainment for a diploma
course, IJECET, IAEME, Vol 6, Issue 2, Feb 2015, pp. 06-12

[12] Student performance using Bloom’s cognition levels: A Case Study, H
R Bhagyalakshmi, D Seshachalam, Journal of Engineering Education Transformations,
Special Issue: Jan. 2015, eISSN 2394-1707

[13] Evaluation of Final Examination Papers in Engineering: A Case Study
using Bloom’s Taxonomy, Arthur James Swart, Member, IEEE

[14] Bloom`s Taxonomy: Improving Assessment and Teaching-Learning
Process, Muhammad Tufail Chandio, Saima Murtaza Pandhiani, Rabia Iqbal, Journal
of Education and Educational Development, Jan 2017

[15] Facial Expression Recognition Using
Static Facial Images, G.Sowmiya , V. Kumutha, Section: Research
Paper, Product Type: Isroset-Journal

Vol.6 , Issue.2 , pp.72-75, Apr-2018

[16] Neural Network through Face
Recognition, A.K.Gupta 1 , S.Gupta, Section:Research
Paper, Product Type: Isroset-Journal

Vol.6 , Issue.2 , pp.38-40, Apr-2018

[17]
Image
Merging in Transform Domain S. Jadav1* , P. Rawool2 , V. Shah3, IJSRNSC
Volume-5, Issue-1, April- 2017

[18] A Philosophical Review On Different Face Recognition
Techniques,  D.
Venkat Ravi Kumar, K. Raja
Sravan Kumar

[19]
INTERNATIONAL JOURNAL OF COMPUTER SCIENCES AND ENGINEERING 6(7):929-933 · July
2018

Vidyadhari S., Dhirendra S. Mishra, "Analysis of Facial Expressions for Predicting Student’s Learning Level," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.87-92, 2020.

In recent years’ traffic congestion is a major problem in our country, which affects modern city’s daily life routine and environments. Due to population growth, the size of cities expands, automatically the number of vehicles increases in the major scale on roads. So, traffic monitoring and controlling is the biggest challenge on traffic management authorities. In many urban areas, most of the traffic signal lights are based on a fixed cycle protocol, which is a reason for the inefficient controlling. Thus, there is an immoderate need to enhance and automate these traffic systems. We design and develop a system for real-time traffic monitoring using the Internet of Things (IoT) platform and sensing technology. Proposed work identifies the traffic conditions based on vehicle numbers on road and makes decisions for a control system with sharing their data synchronously with each other. In addition, the proposed system can send traffic signal data to other signals and people can retrieve traffic situation anywhere. This paper provides an insight into how these technologies can be applied and how it helps to overcome traffic congestion to reduce travel time.

Internet of Things (IoT), Sensors, Raspberry Pi, Pi-Camera, RFID, Synchronous Traffic Monitoring, Vehicle Counting

[1]    
Emad
Issa, Abdul Kaream and Aman Jantan, “Intelligent Traffic Light Control Using
Neural Network with Multi-Connect Architecture”, National Conference on Information
Retrieval and Knowledge Management (CAMP08) in Kuala Lumpur,Malaysia, 2008.

[2]    
S. An, B.-H. Lee, and D.-R. Shin, “A survey of
intelligent transportation systems,” 2011 Third Int. Conf. Comput. Intell.
Commun. Syst. Networks, pp. 332–337, 2011.

[3]    
K. Ashton, “That Internet of Things Thing”, RFiD
Journal, Vol. 22, pp. 97 114, 2009.

[4]    
X. Jia, O. Feng, T. Fan, and Q. Lei, “RFID Technology
and its Applications in Internet of Things (IoT)”, 2nd IEEE conference on
Consumer Electronics, Communications and Networks (CECNet’12), pp. 1282 - 1285,
2012.

[5]    
C. Sun, “Application of RFID Technology for Logistics
on Internet of Things”, AASRI Conference on Computational Intelligence and
Bioinfor- matics, pp. 106 - 111, 2012.

[6]    
J. He, S. Ji, Y. Pan, and Y. Li, “Constructing
Load-Balanced Data Aggre- Gation Trees in Probabilistic Wireless Sensor
Networks”, IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol.
25, No. 7, pp. 1681 - 1690, July, 2014.

[7]    
J. He , S. Ji, R. Beyah, Y. Xie, and Y. Li, “Constructing
Load-Balanced Virtual Backbones in Probabilistic Wireless Sensor Networks via
Multi- Objective Genetic Algorithm”, Transactions on Emerging Telecommunica-
tions Technologies (ETT), Vol. 26, No. 2, pp. 147 - 163, February, 2015.

[8]    
T.G. Zimmerman, “Personal area networks: Near-Field
Intrabody Communication”, IBM System Journal, Vol. 35, pp. 609 617, 1996.

[9]    
P. Baronti, P. Pillai, V.W. Chook, S. Chessa, A.
Gotta, and Y.F. Hu, “Wireless Sensor Networks: A Survey on the State of the Art
and The 802.15. 4 and ZigBee Standards”, Computer Communication, Vol. 30, pp,
1655 1695, 2007.

[10] 
Bluetooth, S.I.G. Specification of the Bluetooth
System, version 1.1. Available online: http://www.bluetooth.com.

[11] 
G. Anstasi, M. Conti, E. Gregori, and A. Passarella,
“802.11 Power-Saving Mode for Mobile Computing in Wi-Fi Hotspots: Limitations, Enhancements
and Open Issues”, Wireless Networking, Vol. 14, pp. 745 768, 2008.

[12] 
M.K. Karakayali, G.J. Foschini, and R.A.
Valenzuela, “Network Coordination for Spectrally Efficient Communications in Cellular
Systems, Wireless Communication, Vol. 13, pp. 56 61, 2006.

[13] 
J. He, S. Ji, R. Beyah, and Z. Cai, “Minimum-Sized
Influential Node Set Selection for Social Networks under the Independent
Cascade Model”, ACM MOBIHOC 2014, pp. 93 - 102, 2014.

[14] 
J. A. Gonzalez-Martnez, M. L. Bote-Lorenzo, E.
Gomez-Sanchez, and R. Cano-Parra, “Cloud Computing and Education: A State of the
Art Survey”, Computers & Education, Vol. 80, pp. 132 - 151, 2015.

[15] 
Misbahuddin, S., Zubairi, J.A., Saggaf, A., Basuni,
J., Sulaiman, A. and Al-Sofi, “IoT Based Dynamic Road Traffic Management for Smart
Cities”, 12th International Conference on High-Capacity Optical Networks and
Enabling/Emerging Technologies, 2015.

[16] 
Choosri, N., Park, Y., Grudpan, S., Chuarjedton,
P. and Ongvisesphaiboon, “IoT RFID Testbed for Supporting Traffic Light Control”,
International Journal of Information and Electronics Engineering 2015.

[17] 
Togrikar, P.S. “Implementing Intelligent Traffic
Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle
Detection”, Imperial Journal of Interdisciplinary Research 2016.

[18] 
Nayak, R. R., Sahana, S. K., Bagalkot, A. S.,
Soumya M., Roopa, J., Govinda, R. M., & Ramavenkateswaran, “Smart Traffic Congestion
Control Using Wireless Communication”, International Journal of Advanced
Research in Computer and Communication Engineering, 2013.

[19] 
Sherly, J. & Somasundareswari, D., “Internet
Of Things Based Smart Transportation Systems”, International Research Journal
of Engineering and Technology (IRJET), 2015.

[20] 
Baskar, L. D., De Schutter, B., Hellendoorn, J.,
& Papp Z., “Traffic Control and Intelligent Vehicle Highway Systems: A Survey”,
IET Intelligent Transport Systems, pp. 38-52, 2011.

[21] 
Cárdenas-Benítez, N., Aquino-Santos, R.,
MagañaEspinoza, P., Aguilar-Velazco, J., Edwards-Block A., & Medina Cass, “Traffic
Congestion Detection System Through Connected Vehicles and Big Data Sensors”, International
Journal of Science and Research (IJSR), 2016.

[22] 
Perumalla, B. K., & Sunil Babu, “An
Intelligent Traffic and Vehicle Monitoring System using Internet of Things
Architecture”, International Journal of Science and Research (IJSR), 2015.

[23] 
“Integrating IR Sensors with a Microcontroller
Automated Regulation of Traffic Flow”, Robert H. Winston 2004.

Hemlata Kachhavey, Mubeen Ahmed Khan, "IOT Based Synchronous Smart Traffic Monitoring System with Data Sharing Capability," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.93-98, 2020.

Agriculture plays an important role in our country, crops are considered to be vital as they are the source of energy to mankind. Due to environmental conditions, crops are getting affected with many diseases. Farmers are not able to detect these diseases at an early stage. Disease in a crop leads to low productivity. Thus, assessment of crop condition is vital. Quality assessment of crops deals with assessing the quality and minimizing the loss of crops. It provides the fundamental information for understanding the quality of the crops and its diseases. There are various Machine Learning algorithms for detection and classification of diseases. Use of machine learning algorithms like CNN not only yields better results but it is also a cost efficient solution and it analyzes the data from different aspects, and classifies it into one of the predefined set of classes. In machine learning, Convolutional Neural Networks are complex feed forward neural networks. CNNs are used for image classification and recognition because of its high accuracy. CNN follows a hierarchical model which works on building a network and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed. CNN outperforms most of the ML algorithms when it comes to image classification provided there are large number of images present in the dataset. The morphological features and properties like color, intensity and dimensions of the plant leaves are taken in to consideration for classification. Thus, detection of disease in early stage will be beneficial for farmer so that necessary actions can be taken.

Machine Learning, Segmentation, Clustering, CNN

[1]    
R. Poonguzhali, A.Vijayabhanu, “Crop Condition Assessment using Machine Learning”,International
Journal of Recent Technology and Engineering (IJRTE), Vol.7, Issue-6S2, April 2019.

[2]    
SrdjanSladojevic, Marko Arsenovic, AndrasAnderla,
DubravkoCulibrk, and Darko Stefanovic, “Deep
Neural Networks Based Recognition of Plant Diseases by Leaf Image
Classification ”, Hindawi Publishing Corporation Computational Intelligence
and Neuroscience, Volume 2016, 29 May 2016.

[3]    
Omkar Kulkarni, “Crop
Disease Detection using Deep Learning”, 2018 Fourth International Conference on Computing Communication
Control and Automation (ICCUBEA)

[4]    
Nikhil Shah, SarikaJain,“Detection of Disease in Cotton Leaf using Artificial Neural Network”,
IEEE 2019

[5]    
Shima Ramesh, Mr. Ramachandra Hebbar, Mr. P V Vinod, “Plant Disease Detection Using Machine
Learning”, 2018 International
Conference on Design Innovations for 3Cs Compute Communicate Control.

[6]    
Radhika Deshmukh,
ManjushaDeshmukh, “Detection of Paddy
Leaf Diseases”, in: International Conference on Advances in Science and
Technology, 2015.

[7]    
P. R.
Reddy, S. N. Divya, and R. Vijayalakshmi, “Plant
disease detection techniquetool—a theoretical approach,” International Journal of Innovative
Technology and Research,pp.
91–93, 2015.

[8]    
P. R. Rothe and R. V. Kshirsagar, “Cotton Leaf Disease Identification using Pattern Recognition
Techniques”, International Conference onPervasive Computing (ICPC),2015.

[9]    
Pasalu IC, Mishra B, Krishnaiah NV, Katti G, “Integrated pest management in rice in India:
Status and prospects”. In: Birthal PS, Sharma OP (eds) Proceedings 11.
Integrated pest management in Indian agriculture. National Centre for
Agricultural Economics and Policy Research (NCAP)/National Centre for
Integrated Pest Management (NCIPM), New Delhi, pp. 25–49,2004

[10]  Singh, V., &
Misra, A. K , “Detection of plant leaf
diseases using image segmentation and soft computing techniques”.
Information processing in Agriculture, 4(1), 41-49, 2017

[11]  Prajapati, H. B.,
Shah, J. P., & Dabhi, V. K , “Detection and classification of rice plant
diseases”. Intelligent Decision Technologies, 11(3), 357-373, 2017

[12]  Mohan K. J,
Balasubramanian M , & Palanivel S, “Detection
and recognition of diseases from paddy plant leaf images”. International
Journal of Computer Applications, 144(12),
2016.

[13]  Ferentinos, K. P, “Deep learning models for plant disease
detection and diagnosis”. Computers and Electronics in Agriculture, 145, 311-318, 2018.

[14]  Zhang S, Huang W,
Zhang C,“Three-channel
convolutionalneural networks for vegetable leaf disease recognition”. Cognit
Syst Res 2019;53:31–41.

[15]  Barbedo JGA, “Plant disease identification from individual
lesions and spots using deep learning”. Biosyst Eng 2019;180:96–107

[16]   Liu B, Zhang Y, He D, Li Y, “Identification of apple leaf diseasesbased
on deep convolutional networks”. Symmetry 2018;10:11

Vandita Mathad, Greeshma R.R., Harshitha J.V., Deepika S., Snigdha Sen, "Quality Assessment of Crops Through Disease Detection Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.99-102, 2020.

In Universities Nationwide, there are a large number of students who pay all their fees through cash deposits, electronic cash transfer or bank drafts to the University’s account in specific bank branches. These methods have proven inefficient in more ways than one. It was upon such background that the researchers embarked on a paper aimed at developing an alternate system that enables students as well as sponsors to securely pay fees using valid credit and debit cards. Object Oriented Analysis and Design Methodology (OOADM) was employed in the design of the new system. The system was developed using hypertext preprocessor (PHP), hypertext markup language (HTML) and MySQL database. The new system assist students in paying their fees and issues them a receipt automatically.

HTML, PHP, OOADM, Management System

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in Ghana: An evaluation of customers’ perceptions. American Academy of Financial Management. 1(3), 335-356.

[2]    
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user-centered perspective and interaction design. Eindhoven: Technische
Universities Eindhoven. Data Library
Eindhoven University of Technology.47-51.

[3]    
Adeoti, O. and Osotimehin, K. (2012). Adoption of Point
of Sales Terminals in Nigeria: Assessment of Consumers’ Level of Satisfaction. Research Journal of Finance and Accounting,
3(1), 1-5.

[4]    
Agimo, J. (2004). Better practice checklist for
e-payment. Australian Government
Information Management Office Archive.

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Ajayi, S., & Ojo, O. (2006). Money and Banking:
Analysis and Policy in the Nigerian Context. International Journals of Business and Finance Management Research.
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[6]    
Antwi, S., Hamza, K., & Mehrabi, J. (2013). Examining
the Effectiveness of Electronic Payment System in Ghana: The Case of e-ZWICH in
the Tamale Metropolis. Research Journal
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&Prased, R. (2001). Electronic banking in Malaysia: A Note on Evolution of
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Banking On Mobile to Deliver Financial Services to the Poor. Global Financial Inclusion, 1, 24-31.

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Affect Students’ Use of Online Registration: An Exploratory Study. Association
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Chavosh, A., Halimi, A.and Espahbodi, S. (2011).
Comparing the Satisfaction with the Banks E-payment Services between Degree
Holder and Non-Degree Holder Customers in Penang-Malaysia. International Journal of e-Education, e-Business, e-Management and e-Learning.,
1(2), 132-138.

[13] 
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M-commerce payment systems through the analytic hierarchy process. Journal of Business Research, 57(12),
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Amanze Bethran Chibuike, Nwoke, Bethel Chinenye, Eleberi Leticia E., "An Online Departmental Fee Management System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.103-109, 2020.