Volume-8 , Issue-3 , Mar - 2020 Go Back

Among worldwide, agriculture has the major responsibility for improving the economic contribution of the nation. However, still the most agricultural fields are under developed due to the lack of deployment of ecosystem control technologies. Due to these problems, the crop production is not improved which affects the agriculture economy. Hence a development of agricultural productivity is enhanced based on the plant yield prediction. To prevent this problem, Agricultural sectors have to predict the crop from given dataset using machine learning techniques. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information’s like, variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments etc. A comparative study between machine learning algorithms had been carried out in order to determine which algorithm is the most accurate in predicting the best crop. The results show that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with entropy calculation, precision, Recall, F1 Score, Sensitivity, Specificity.

Dataset,Machine learning-classification method

[1]Robotic Agriculture, ser. UK-RAS White Papers. UK-RAS Network,2018.

[2]Priyanka R.R., Mahesh M., Pallavi., Jayapala G., Pooja M.R.Crop protection by an alert based system using deep learning  concept Research Paper|Isroset(IJSRCSE)Vol.6,Issue.6, pp47-49,Dec-2018

[3] P. Lottes, M. Hoeferlin, S. Sander, M. Muter, P. Schulze, and L. C. ¨Stachniss, “An effective classification system for separating sugar beetsand weeds for precision farming applications,” in ICRA, 2016, pp. 5157–5163.

[4] A. Milioto, P. Lottes, and C. Stachniss, “Real-time semantic segmentation of crop and weed for precision agriculture robots leveragingbackground knowledge in cnns,” in ICRA, 2018, pp. 2229–2235.

[5] P. Lottes, J. Behley, A. Milioto, and C. Stachniss,“Fully convolutionalnetworks with sequential information for robust crop and weeddetectionn precision farming,” IEEE Robotics and Automation Letters (RA-L),vol. 3, pp. 3097–3104, 2018.

[6] O. Ronneberger, P. Fischer, and T. Brox, “U-net:Convolutional networksfor biomedical image segmentation,” in International Conference onMedical image computing and computer-assisted intervention, 2015, pp.234–241.

[7] K.Simonyan and A.Zisserman,“Very deep convolutional networks forlarge-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

[8] F.-M. De Rainville, A. Durand, F.-A. Fortin, K. Tanguy, X. Maldague,B. Panneton, and M.-J. Simard, “Bayesian classification and unsupervised learning for isolating weeds in row crops,” Pattern Analysis andApplications, vol. 17, no. 2, pp. 401–414, 2014.

[9] S. Haug, A. Michaels, P. Biber, and J. Ostermann, “Plant classificationsystem for crop/weed discrimination without segmentation,” in WACV.IEEE, 2014, pp. 1142–1149.

[10] P. Lottes, R. Khanna, J. Pfeifer, R. Siegwart, and C. Stachniss, “Uavbased crop and weed classification for smart farming,” in ICRA, 2017,pp. 3024–3031.

[11] S. H. Lee, C. S. Chan, P. Wilkin, and P. Remagnino, “Deep-plant: Plantidentification with convolutional neural networks,” in ICIP. IEEE, 2015

Spoorthi P, Jayashankara M, "A Machine Learning Approach to Predict Crop yeild and Reduction of Cost by Finding Best Accuracy," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.61-66, 2020.

Finding a parking place in a busy city center is often a frustrating task for many drivers, time and fuel are misspent in the search for a vacant slot and traffic jam in the area expand due to the slow moving vehicles circling around. In the existing system sensors are used so it may require frequent maintenance. Although several amount of research works on the development of smart parking system exist in literature, but almost all of them have not addressed the problem of real-time identification of actual parking and automatic collection of parking charges. The shortest path finder for Vehicle Parking System will find the closest path for parking using Dijikstra’s algorithm.   The SPFVP will guide the drivers smartly to their desired parking destination and the driver can park at the reserved space without any searching. GPS technique is used for helping the driver to identify the nearest parking area. Graphical Interface shows the user for the available and reserved spaces that will help the drivers for selecting the suitable space.  

Smart parking, Parking slot, GPS (Global positioning System), IR (Infrared Sensor)

Sharmila Chopade,Rushikesh Nagawade, Mayur Jadhav, Aakash Awate,Indrajeet Chaudhari, "Shortest Path Finder for Vehicle Parking(SPFVP)," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.67-70, 2020.

In 21^st century whole world is dependent on Information Technology where accuracy, trustworthiness, speed have huge impact. The fee collection progression in college is very crucial procedure as fee structure for every student is different. The fee structure & analysis tool is designed and developed for fee distribution sheet and total collection sheet preparation with various reports required at university level and college level. In manual procedure has some chances of human error. This is the reason that many educational institute are shifting from manual process to Fee structure and analysis tool. There are many advantages of Fee structure and analysis tool like paperless system, automatic calculation, and safe and secure etc. The aim of research work is to develop a Fee structure and analysis system tool for college to make fees related activity and process easy, efficient and user friendly.

Fee structure, fee instalments, fee distribution, Flexibility

[1] K.S.R
Anjareyuly, R. Anderson, ”The advantage of Data Flow Diagrams for Beginning
Programming”. Department of psychology Department of psychology, 2008. 

 [2] Awad Elias M. -Structured System Analysis
and Design – Second edition: Galgotia

[3] R. Ibrahim
and S. Yen,” Formalization of the data flow diagram rules for consistency
check”, International Journal of Software Engineering  & Applications (IJSEA), Vol.1, No.4,
October, 2010.

[4] J. Atif , U.
Mohammad , N. Amer, ”Comparative Study on DFD to UML Designs Transformations”,
world of computer science and information technology, ISSN 2221-0741,vol 1,No
1,10- 16,2011

[5]R. Ibrahim, S.
Yen Yen,” An Automatic Tool for Checking Consistency between Data Flow Diagrams
DFDs)”, World Academy of Science, 
Engineering and Technology 69, 2010.

[6]S.V. nikam,
B.T. Jadhav ,“Design and Development of Result Tool for University and College
Exam and it’s Performance Study” / International Journal on Computer Science
and Engineering (IJCSE)

S.S. Patil, R.S. Nikam, "Design and Development of Fee Structure and Analysis Tool," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.71-74, 2020.

In this paper focuses on a system of recognizing human’s emotion detected from a human’s face. The analysed information is conveyed by the regions of the eye’s and the mouth and the image is compared with the database created which consist of various facial expressions pertaining to six universal basic facial emotions. The methodology uses a classification technique of information into a new fused image which is composed of two blocks integrated by the area of the eyes and mouth, very sensitive areas to changes human’s expressions. This system focuses on the facial expressions and by detecting them it helps to relieve the stress of the user by providing the various platforms such as the Chat Bot, Music Player, etc. this is based on the detected expressions of the user and the system uses the machine learning for this purpose. 

Desktop partner, stress relief, emotion detection, etc.

[1] A. Mehrabian, “Communication without words”, psychology today, vol. 2, no. 4, pp. 53-56, 1968. H. Simpson, Dumb Robots, 3rd ed., Springfield: UOS Press, 2004, pp.6-9.

[2] V. Bruce, “What the Human Face Tells the Human Mind: Some Challenges for the Robot-Human Interface”, Proc. IEEE Int. Workshop Robot and Human Communication, pp. 44-51, 1992B. Simpson, et al, “Title of paper goes here if known,” unpublished.

[3] Neha Gupta, Prof. Navneet Kaur, “Design and Implementation of Emotion Recognition System by Using Matlab”, International Journal of Engineering Research and Applications, Vol. 3, Issue 4, pp. 2002-2006, Jul-Aug 2013.

[4] P. M. Chavan, M. C. Jadhav, J. B. Mashruwala, A. K. Nehete, Pooja A. Panjari, “Real Time Emotion Recognition through Facial Expressions for Desktop Devices”, International Journal of Emerging Science and Engineering, Vol. 1, No. 7, May 2013

[5] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, Jul. 1997.  

[6] B.A. Draper, K. Baek, M.S. Bartlett, J.R. Beveridge, “Recognizing Faces with PCA and ICA,” Computer Vision and Image Understanding: special issue on face recognition, in press.  

[7] J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional PCA: A new approach to appearance-based face representation and recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131–137, 2004.

[8] L.Torres, J. Reutter, and L. Lorente, “The importance of the color information in face recognition,” in Proceedings IEEE International Conference on Image Processing, vol. 3, pp. 627–631, 1999

[9] M. A. O. Vasilescu and D. Terzopoulos, “Multilinear image analysis for facial recognition,” in Proc. Int  Conf. Pattern Recognit., Quebec City, QC, Canada, pp. 511–514, Aug. 2002.

[10] A.K. Jain, R.P.W. Duin, J Mao, "Statistical Pattern Recognition: A Review", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 4-37, 2000.

A.S. Gawade, Y.C. Gaikwad, A.T. Lambar, "AI Desktop Partner Facial Expression Detection," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.75-77, 2020.

Data is defined as the collection of facts. When data are refined it becomes information. Data plays a vital role in obtaining any kind of information. These data are collected from different sources. It is difficult to handle these data sometimes. In data mining, all the data are collected from different sources as per their need. From telecommunication to retail industries, from finance to educational institutes all these data give vital information for any kind of execution that needs to be done for the future course of action. However, some challenges need to be taken care of while obtaining these data. Some data might be noisy, some might be scattered. Looking into this scenario we should be very careful while playing with these data. Every organization heavily depends on these data for their planning and execution. Data mining plays a vital role in achieving the target of the organization. It should be handled in a precise manner. Though the challenges are many their significance is equally high. This paper explains the brief ideas on what are the new trends in data mining and how it is helping to overcome our needs as per the current requirements. 

Data Mining, Semantic Web, Multirelational data mining, Bio-informatics, Algorithms, Challenges in data mining.

[1]    
Bharati M.
Ramageri,  “Data Mining Techniques and
Applications ”, Indian Journal of Computer Science and Engineering ,  Vol.1
Issue.4, pp. 301-305, 2012.

[2]    
Jiawei
Han & Micheline Kamber “Data
Mining Concepts and Techniques”, Morgan
Kaufmann Publishers, pp. 400-436,2003.

[3]    
 Sadiq Hussain, “Survey on Current Trends and Techniques of Data Mining Research ”,
London Journal of Research in Computer Science and Technology, Vol.17 Issue.1 ,
pp. 07-13,2017.

[4]    
Rakesh
Kumar Saini, "Data Mining tools and
challenges for current market trends-A Review," International Journal
of Scientific Research in Network Security and Communication, Vol.7, Issue.2, pp.11-14, 2019.

[5]    
Bindushree
V., Rashmi G.R., Uma H.R., “Analysis of
Text Recognition with Data Mining Techniques,” International Journal
of Scientific Research in Computer Science and Engineering, Vol.7, Issue.6,
pp.40-42, 2013.

[6]    
Arun K Pujari, ”Data
Mining Techniques”,University Press,pp. 01-50,2013.

Gagan Gurung, Rahul Shah, Dhiraj Prasad Jaiswal, "Emerging Trends in Data Mining- An Algorithms used, Challenges and its Significance in Current Scenario," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.78-82, 2020.

Agriculture is the largest economic sector and it plays a major role in economic development of India. The manual classification and grading techniques of fruits are distinguished between different types of fruits. Many new technologies are developed by researchers but enhance method is still needed. This paper presents a method for fruit grading from fruit images using image processing and machine learning techniques.

Fruit grading , Fruit images, SVM, Classification and Machine Learning Approach

[1] Rupali S.Jadhav, S. S. Patil, “A Fruit Quality Management System Based On Image Processing”, IOSR Journal of Electronics and Communication Engineering, Vol.2, PP.45-78,2013.
[2] Rashmi Pandey, Sapan Naik , Roma Marfatia, “Image Processing and Machine Learning for Automated Fruit Grading System: A Technical Review”, International Journal of Computer Applications ,Vol. 5 PP. 23-30, 2013.
[3] Manoj B. Avhad , Satish M. Turkane, “ARM Based Fruit Grading and Management System Using Image Processing”, International Journal of Advanced Research in Computer Engineering &Technology (IJARCET) , Volume 2, Issue 1, 2013.
[4] Anuradha P. Gawande , S.S. Dhande, “Implementation of fruit Grading System by Image Processing and Data Classifier- A Review”, International Journal of Engineering Research and General Science, Vol.9, PP. 20-25,2014.
[5] G. Polder,G.W.A.M. van der Heijden,I.T. Young, “Tomato sorting using independent component analysis on spectral images Real-Time Imaging”, IJDR, Vol6. PP. 34-39.,2018.
[6] Agarwal, S.S. Bhadouria. “Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIRA”, International Journal of Scientific Research in Biological Sciences, Vol.5 , Issue.2 , pp.56-62, Apr-2017.
.[7] K. Jain, N. Tripath,” Speech Features Analysis and Biometric Person Identification in Multilingual Environment”, IJSRNSC, Vol.6 , Issue.1 , pp.7-11, Feb-2018.

Priyanka Banerjee, Souvik Sur, Kashi Nath Dey, Samir Kumar Bandyopadhyay, "A Proposed Method for Fruit Grading from Fruit Images using SVM," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.83-88, 2020.

The Growing Interest of computer vision within the past decade. From a preferred computer vision is an area of research , it has been found that a complex problem of computer vision is rise above by face detection and recognition and it provide us one of the better and successful analysis the image of applications and unable to understand the algorithm. Because of the essential nature of the problem, In area of research computer science and computer vision make use of neuro-scientific and psychological studies. It mainly use for the purpose of processing the computer image that advances in nature and help us to understand the research that provide awareness that how our brain work and vice versa. Many developer have various purpose of making different applications for the users that can be access on different platforms which mainly work to analysis carefully the facial features of the person. This application based on computer vision which is an open source named as Intel’s , OpenCV and framework.

face detection, face recognition, opencv, NumPy

[1] B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” Proceedings of the DARPA imaging understanding workshop, pp. 121–130, 1981.
[2] Lucas-Kanade 20 years on: A unifying framework” Baker and Mathews, IJCV 04.
[3] Lyons, Michael J., Shigeru Akamatsu, Miyuki Kamachi, Jiro Gyoba, and Julien Budynek. ”The Japanese female facial expression (JAFFE) database.” third international conference on automatic face and gesture recognition, pp. 14-16, 1998.
[4] Ian Goodfellow et al. Challenges in Representation Learning: A report on three machine learning contests, 2013
[5] Y. Tian, T. Kanade and J. Cohn, “Recognizing Action Units for Facial Expression Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 97–115, 2001.
[6] Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. “Spatial transformer networks.” Advances in neural information processing systems, 2015.
[7] Shhervin Minaee, Amirali Abdolrashidi, “Deep emotion:Facial Expression Recognition Using Attentional Convolutional Network”.
[8] J.Y. Bouguet, “Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm,” Intel Corporation, Microprocessor Research Labs, pp. 1-9, 2000.
[9] Lucey, Patrick, et al. “The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression.” Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference on. IEEE, 2010.
[10] Vinay Bettadapura,“Face Expression Recognition and Analysis:The State of the Art”.
[11] Octavio Arriaga, Paul G. Ploger, Matias Valdenegro, “Real Time convolutional Neural Networks for Emotion and Gender Classification”.
[12] Shan, Caifeng, Shaogang Gong, and Peter W. McOwan. “Facial expression recognition based on local binary patterns: A comprehensive study.” Image and vision Computing 27.6: 803-816, 2009.
[13] Dario Amodei et al. Deep speech 2: End-to-end speech recognition in english and mandarin. CoRR, abs/1512.02595, 2015.
[14] Official site of Opencv.
[15] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pages 315–323, 2011.
[16] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2818–2826, 2016.
[17] N.Sudhakar Reddy, M.V.Sumanth, S.Suresh Babu, "A Counterpart Approach to Attendance and Feedback System using Machine Learning Techniques",Journal of Emerging Technologies and Innovative Research (JETIR), Volume 5, Issue 12, Dec 2018.
[18] Dan Wang, Rong Fu, Zuying Luo, "Classroom Attendance Auto-management Based on Deep Learning",Advances in Social Science, Education and Humanities Research, volume 123,ICESAME 2017.
[19] Akshara Jadhav, Akshay Jadhav, Tushar Ladhe, Krishna Yeolekar, "Automated Attendance System Using Face Recognition", International Research Journal of Engineering and Technology (IRJET), Volume 4, Issue 1, Jan 2017.
[20] B Prabhavathi, V Tanuja, V Madhu Viswanatham and M Rajashekhara Babu, "A smart technique for attendance system to recognize faces through parallelism", IOP Materials Science and Engineering 263, 201

Anjali Rai, Ayushi Chauhan, Deepak Chaudhary , "Attendance System Based on Face Recognition," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.89-94, 2020.

Cloud computing being the latest technology, the storage and retrieval of  personal data from mobile devices can be done from any location at anytime. The data security problem in mobile cloud is preventing development of mobile cloud. Studies  have been conducted in various aspects to provide improved solutions to the cloud security. As mobile devices only have limited power and computing resources most of the solutions are not favorable for mobile cloud.. In this paper, we propose a reliable lightweight data exchange scheme for mobile cloud computing. The results show that the overhead on the mobile device side when users are sharing data in mobile cloud environments is effectively reduced using this technology.If  the user wants to upload or download the data publicly then there wont be any encryption made over that data and if the data is confidential then certain algorithms for encryption can be applied to maintain the integrity.

Reliable, Integrity,Encrypted Data

[1]
Ruixuan Li, Member,IEEE, Chenglin Shen, Heng He, Zhiyong Xu, and Cheng-Zhong
Xu, Member, IEEE,“A Lightweight Secure Data Sharing Scheme for Mobile Cloud
Computing”,Volume: 6 , Issue: 2 , April-June 1
2018.

[2]Cheung,
L., Newport, C.: Provably secure ciphertext policy abe. In: Proceedings of the
14th ACM Conference on Computer and Communications Security, CCS 2009, pp.
456–465. ACM, New York (2007)

[3]Shubham Chandugade, Prachi More, Shaikh
Mohammad Shafi Rafiq ,IRJET,”Survey
on Lightweight Secured Data Sharing Scheme for cloud computing”, Volume: 04 Issue: 10,
p-ISSN: 2395-0072,Oct -2017

[4] K.
Liang et aI., "A OFA -based functional proxy re-encryption scheme for
secure public cloud data sharing," IEEE Trans. Inf. Forensics Security,vol.
9, no. 10, pp. 1667-1680, Oct. 2014.

[5] H.
Hong, Z. Sun. "An efficient and traceable KP-ABS scheme with untrusted
attribute authority in cloud computing", JoCCASA, 5(2).pp.I -8,201 6.

[6] J.
Liu, X. Huang, and 1. K. Liu, "Secure sharing of personal health records
in cloud computing: Cipher textpolicy attribute-based signcryption,"
Future Gene rat. Com put. Syst., vol. 52, pp. 67-76, Nov. 2015.

K.S. Bhosale, M.R. Kshirsagar, K.L. Jadhav, S.G. Sayyad, "Reliability and Integrity in Lightweight Data Exchange for Mobile Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.95-96, 2020.

Optical fiber communication along with the advanced optical networking technologies and advanced modulation formats is an evolving field of research. The enormous bandwidth and long distance reach of optical networks makes them a suitable candidate to be used in backhaul networks. This paper presents a comprehensive review of various advanced optical multiplexing technologies viz: Wavelength Division Multiplexing (WDM), Orthogonal Frequency Division Multiplexing (OFDM), Time Division Multiplexing (TDM) along with several architectural advancements in these optical multiplexing networks is also discussed in a holistic manner.

WDM, RoF, PON, EONs, Orthogonal Frequency Division Multiplexing (OFDM), TDM

[1]    
Mukherjee B, “ WDM optical communication
networks:   Progress and challenges”,
IEEE Journal on selected areas in communications, VOL.18, No. 10,pp 1810-1824,
2000.

[2]    
Prachi Sharma ,”A
review of development in the field of fiber optic communication systems”,
International Journal of Emerging Technology and Advanced Engineering, Vol. 3,
no. 5, pp. 113-119, 2013.

[3]    
 Aparna Tomar, Dr. Vandana, Vikas Thakare,” A
Review on advancement in optical communication system”, International journal
of advanced engineering research and science, ISSN(O): 2456-1908, vol 4, issue
5, may 2017.

[4]    
Manabu kagami,
“Visible optical fiber communication”, R&D Review of Toyota CRDL, VOL 40
NO. 2, Special issue.

[5]    
N. Baharudin, R.
Alsaquor, H. Shakar, O. Alsaquor, T. Alahdal, “Review over multiplexing
technologies in bandwidth utilization” Middle east Journal of scientific
research 18(10): 1510-1516, 2013.

[6]    
Parul,”Wavelength
division multiplexing: An overview and recent developments”, Int. Journal of
Engineering Research and applications ISNN: 2248-9622, Vol. 4, Issue 5(version
4), May 2014, pp. 114-117.

[7]    
”CWDM and DWDM
networks- Which is best for you?”, Technical Articles, Vitextech.

[8]    
Vinita Khosia ,” A
Comprehensive Review of Recent Advancement in Optical Communication Networks”,
International Journal of Computer Sciences and Engineering, e-ISSN: 2347-2693,
vol. 6(9), sept. 2018

[9]    
Diksha Sharma,
Ashish Sachdeva ,” wavelength Division Multiplexing”, International Journal of
Advanced Research in Electronics and Communication Engineering, ISSN: 2278-909X
, vol 5 issue 5 , May 2018

[10] 
Amrita Soni, Neha
Prajapati, Dr. Neha Sharma,” A review article of WDM based optical fiber
communication with WDM system evolution”, International journal of advance
research , ideas and innovations in technology, ISSN: 2454-132X , volume 4,
issue 3, 2018.

[11] 
Deepak S. and
Suresh K.(2017). An Overview of Elastic Optical Networks and its Enabling
Technologies. International Journal of Engineering and Technology,9(3),1643-1649.
DOI: 10.21817/ijet/2017/v9i3/170903022

[12] 
Deepak S.,Payal
and Rajbir S.(2017).Performance Investigation of Dispersion Compensation
Techniques in 32 channel DWDM Systems. International Journal of Electronics
Electrical and Computational Systems(ijeecs),7(8), 420-427

[13] 
Payal, Rajbir S.
&Deepak S.(2017).Performance Comparison of Hybrid Optical Amplifiers with
different Modulation Formats. International Journal of Electronics Electrical
and Computational Systems(ijeecs), 6(11), 315-320

[14] 
Payal, Suresh K.,
Deepak S.(2017). Performance Analysis of NRZ and RZ Modulation Schemes in
Optical Fiber Link Using EDFA. International Journal of Advanced Research in
Computer Science and Software Engineering, 7(8), 161-168. DOI:
10.23956/ijarcsse/V7I8/0102.

[15] 
J. Armstrong,”OFDM
for Optical Communication”,J. Lightw. Technol.,vol. 27, no. 3,pp. 189-204,
2009.

[16] 
Wei Ji, Xiao Li,
Zhaoyuan Kang, Xuwei Xue,”Design of WDM-RoF-PON based on improved OFDM
mechanism and optical coherent technology”, Journal of Optical Communications
and Technology, volume 7, issue 2, pp. 74- 82(2015) .

[17] 
Deepak S. and
Suresh K. (2017),”Design and Evaluation of OFDM based Optical Communication
Network” Journal of Engineering and Applied Sciences,12, 6227-6233  DOI:10.3923/jeasci.2017.6227.6233

[18] 
Alcardo Alex
Barakabitze, Md Abbas Ali, “ Behavior and techniques for improving performance
of OFDM systems for wireless communications ”International Journal of Advanced
Research in Computer and Communication Engineering, ISSN(online): 2278-1021,
vol.4 issue 1, Jan 2015

[19] 
Beena R. Ballal,
Ankit Chadha, Neha Satam,” Orthogonal frequency division multiplexing”,
International Journal of Science and Research , ISSN(online): 2319-7064, volume
2 issue 1 , January 2013.

[20] 
C. Zhang, J.
Huang, C. Chen and K. Qui,” All optical virtual private network and ONUs
communication in optical OFDM-based PON system” Opt. Express 19,
24816-24821(2011).

[21] 
Gill R. Tsouri and
Dov Wulich,”Securing  OFDM over Wireless
Time-varying Channels using subcarrier overloading with joint signal constellation”,
Hindawi Publishing Corporation, EURASIP Journal on Wireless Communication and
Networking, volume 2009, article ID 437824, doi:1155/2009/437824

[22] 
Haji M. Furqan,
Jehad M. Hamamreh and Huseyin Arslan,”Adaptive OFDM-IM for enhancing physical
layer security and spectral efficiency of future wireless networks” Hindawi,
Wireless communications and mobile computing, volume 2018, article ID 3178303

[23] 
Pedram Kheirkhah
Sangdeh and Huacheng Zeng (September 4th 2019),” Overview of Multiplexing
Techniques in Wireless Networks, Multiplexing”, Somayeh Mohammady, Intech Open,
DOI: 10.5772/intechopen.85755.

[24] 
Raj Hakani,”
optical Time Division Multiplexing (OTDM)”, Seminar report

[25] 
Mostafa A.
El-Aasser,” Time division multiplexing and its application in high speed
optical communications” , UMI No. : 3054236, 2002.

[26] 
S. Khant, A.
Patel, “Designing four channel High rate TDM passive optical network with NRZ
scheme for wired environment”, International Journal of applied Engineering and
research ISSN: 0973-4562 Volume 12, no. 22(2017) pp. 12746-12751.

[27] 
Deepak S., Payal
and Suresh K.(2018). Q factor based performance evaluation of bidirectional TDM
PON Network using Hybrid Amplifier Configurations. International Journal of
Computer Science and Engineering. Vol. 6 Issue 4,pp (51-60) April, 6(4), 51-60.
Doi: 10.26438/ijcse/v6i4.5160

Ravinshu, Anjali, Pooja, "A Review of Recent Advancement in Optical Multiplexing Technologies," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.97-102, 2020.

Conserving energy so that data collection can be prolonged is discussed through this paper. Lack of time and hectic life causes disease to spread at wild rates. To this end, technology plays critical role in the detection and prevention of diseases. IoT(internet of things) is an emerging field that operates on real time datasets to detect any abnormality through classification modeling. Sensor is an integrated component of IoT that is used to collect data and then store within data store. Sensors have limited energy associated with them. There are number of mechanisms including LEACH, DEEC, MDEEC, EDEEC etc. all these mechanisms conserve energy but optimization in each protocol is missing. Problems associated with listed protocols are discussed and mechanisms used to overcome the problems are also briefed. Comparative analysis suggests DEEC protocol is best among all and can be used for optimization purpose. Health predictions will be better in case sensors are in good health.

Parameters: Residual Energy, Lifetime, Packets sent to base station, packet sent to controller

Achievement: The health of sensors and lifetime of network is increased through optimization mechanisms- GA, PSO, ACO

Optimization Algorithms, Sensors, Health Monitoring, Packets to controller, Packets to base station, Energy Efficiency

[1]   R.
Kumar, “Evaluating the Performance of DEEC Variants,” IEEE ACcess, vol.
97, no. 7, pp. 9–16, 2014.

[2]   T. Tiwari and N. R. Roy,
“Modified DEEC: A varying power level based clustering technique for WSNs,” 2015
Int. Conf. Comput. Comput. Sci. ICCCS 2015, pp. 170–176, 2015.

[3]   S. Kumar, “DE-LEACH : Distance
and Energy Aware LEACH,” IEEE, vol. 88, no. 9, pp. 36–42, 2014.

[4]   E. Cheikh, C. Saad, B. Mostafa,
and H. Abderrahmane, “Energy Efficient Enhancement of TDEEC Wireless Sensors
Network Protocol Based on Passive RFID Implementation,” vol. 3, no. 5, pp.
6647–6653, 2014.

[5]   Abdul Razaque, Musbah
Abdulgader, Chaitrali Joshi, Fathi Amsaad, and Mrunal Chauhan, “P-LEACH: Energy
Efficient Routing Protocol for Wireless Sensor Networks,” 2016 IEEE Long
Isl. Syst. Appl. Technol. Conf., pp. 1–5, 2016.

[6]   D. R. Prasad, P. V
Naganjaneyulu, and K. S. Prasad, “Modi fi ed LEACH Protocols in Wireless Sensor
Networks — A Review,” IEEE ACcess, pp. 681–688, 2018.

[7]   J. Joshi, J. Rathod, and K.
Wandra, “Performance Enhancement of LEACH for Secured Data Transmission,” IEEE
ACcess, vol. 10, no. May, pp. 2–5, 2017.

[8]   S. Mahajan, “An energy balanced
QoS based cluster head selection strategy for WSN,” Egypt. Informatics J.,
vol. 15, no. 3, pp. 189–199, 2014.

[9]   T. Amgoth, P. K. Jana, and S.
Thampi, “Energy-aware routing algorithm for wireless sensor networks,” Comput.
Electr. Eng., vol. 41, no. C, pp. 357–367, 2015.

[10] P. Saini and A. K. Sharma,
“E-DEEC - Enhanced distributed energy efficient clustering scheme for
heterogeneous WSN,” 2010 1st Int. Conf. Parallel, Distrib. Grid Comput. PDGC
- 2010, pp. 205–210, 2010.

[11] A. Preethi, E. Pravin, and D.
Sangeetha, “Modified balanced energy efficient network integrated super
heterogeneous protocol,” 2016 Int. Conf. Recent Trends Inf. Technol. ICRTIT
2016, 2016.

[12] S. Chand, S. Singh, and B.
Kumar, “Heterogeneous HEED protocol for wireless sensor networks,” Wirel.
Pers. Commun., vol. 77, no. 3, pp. 2117–2139, 2014.

[13] A. Elghirani, R. Subrata, A. Y.
Zomaya, and A. Al Mazari, “Performance enhancement through hybrid replication
and genetic algorithm co-scheduling in data grids,” 16th IEEE/ACS Int. Conf.
Comput. Syst. Appl., pp. 436–443, 2018.

[14] W. Wen, C. Wang, D. Wu, and Y.
Xie, “An ACO-Based Scheduling Strategy on Load Balancing in Cloud Computing
Environment,” IEEE Access, 2015.

[15] M. El Fissaoui, A. Beni-hssane,
and M. Saadi, “Mobile Mobile Agent Agent Protocol Protocol based based energy
energy aware aware data data Aggregation Aggregation for for wireless wireless
sensor sensor networks networks,” Procedia Comput. Sci., vol. 113, pp.
25–32, 2017.

[16] M. Ibragimov, J. Lee, M.
Kalyani, J. Namgung, S. Park, O. Yi, C. H. Kim, and Y. Lim, “CCM-UW Security
Modes for Low-band Underwater,” Wirel. Pers. Commun., 2016.

[17] M. Arioua, Y. Assari, I.
Ez-zazi, and A. Oualkadi, “Multi-hop cluster based routing approach for
wireless sensor networks,” Procedia - Procedia Comput. Sci., vol. 83,
no. Ant, pp. 584–591, 2016.

[18] P. Li, W. Jiang, H. Xu, and W.
Liu, “Energy Optimization Algorithm of Wireless Sensor Networks based on
LEACH-B,” IEEE ACcess, 2017.

[19] C. Discipline, “6dqwdu 3do
6lqjk,” vol. 45, pp. 687–695, 2015.

[20] R. Rohankar, C. P. Katti, and S.
Kumar, “Comparison of Energy Efficient Data Collection Techniques in Wireless
Sensor Network,” Procedia - Procedia Comput. Sci., vol. 57, pp. 146–151,
2015.

[21] P. C. Science, T. Authors, E. B.
V This, C. C. By-nc-nd, C. P. Chairs, T. Authors, E. B. V This, C. C. By-nc-nd,
and C. P. Chairs, vol. 52, pp. 641–646, 2015.

[22] Q. Xu and J. Zhao, “Multi-Head
Track-Sector Clustering Routing Algorithm In WSN,” ACM, no. Icitmi, pp.
707–713, 2015.

[23] M. A. Jan, P. Nanda, X. He, and
R. P. Liu, “PASCCC: Priority-based application-specific congestion control
clustering protocol,” Comput. Networks, vol. 74, no. PB, pp. 92–102,
2014.

[24] R. Pal, R. Sindhu, and A. K.
Sharma, “SEP-E ( RCH ): Enhanced Stable Election Protocol Based on Redundant
Cluster Head Selection for HWSNs,” pp. 104–114, 2013.

[25] A. Singh and A. K. Singh,
“Clustering Protocol for Wireless Networks,” pp. 365–374.

[26] A. A. Ibrahim and A. K. Tamer,
“SEC-TEEN : A Secure Routing Protocol for Enhanced Efficiency in Wireless
Sensor Networks,” pp. 621–631.

[27] M. Liu, J. Cao, G. Chen, and X.
Wang, “An Energy-Aware Routing Protocol in Wireless Sensor Networks,” IEEE,
pp. 445–462, 2009.

[28] F. Shang, “A Single-Hop Active
Clustering Algorithm for Wireless,” IEEE ACcess, pp. 397–406, 2009.

[29] T. Shah, N. Javaid, and T. N.
Qureshi, “Energy Efficient Sleep Awake Aware ( EESAA ) Intelligent Sensor
Network Routing Protocol,” IEEE ACcess, pp. 1–6, 2016.

[30] S. Ozdemir, “Secure and Reliable
Data Aggregation for,” pp. 102–109, 2007.

[31] D. Advisor and D. Committee,
“Communication Security in Wireless Sensor,” 2007.

Dazy Kohli, Deepak, "Hybridization of Energy Efficient Clustering and Multi-Heuristic Strategies to Increase Lifetime of Network," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.103-108, 2020.