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In this paper, the input and output scaling factors of the type-2 fuzzy PID Controller (IT2-FPID) are determined using three different optimization algorithms (Cuckoo search (CS), Particle swarm optimization (PSO), and Bee colony algorithm (BCA)) for a first-order integrating plus dead time (FOIPD) model. A comparative performance study is made for these three optimization algorithms in terms of various transient performance indices. The comparative analysis on the experimental results reveals that BCA based optimal IT2-FPID shows better performance on a simulation model whereas CS based optimal IT2-FPID is found to be superior for practical system over other algorithms.

Particle swarm optimization(PSO), Cuckoo search algorithm (CS), Bee colony algorithm(BCA), Interval type-2 fuzzy controller.

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Radpukdee " Sliding Mode Control with 
PID Tuning Technique: An Application to a DC Servo Motor Position
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Mohd
Fua’ad Rahamat & Mariam md Ghazaly, “Performance Comparison between PID And
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Bandyopadhyay, A. Das, “Emphasis on Genetic Agorithm (GA) over Different PID
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Ontiveros-Robles, P. Melin, O. Castillo, “Comparative Analysis of Noise
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B.
Sakalli, T. Kumbasar, “On the design and gain analysis of IT2-FPID with a case
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Castillo, L. Amador-Angulo, “A generalized type-2 fuzzy logic approach for
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M.
Konar, A. Bagis, “Performance Comparison of Particle Swarm Optimization,
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Ritu Rani De (Maity), Rajani K. Mudi , Chanchal Dey, "Comparative Performance Study of Optimal Interval Type-2 Fuzzy PID Controllers with Practical System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.1-6, 2020.

Use of Precision Agriculture (PA) is the need of an hour to enhance the crop productivity to meet the increasing demand of food supply. Clustering algorithms have been proven to be the best suitable ones to delineate the management zones (as per soil fertility) in PA. Management zones can be treated as sub-fields, which are homogeneous in soil physical/chemical properties. In this paper we have proposed a median strange point (MSP) clustering algorithm for the delineation of agricultural management zones. The median strange point algorithm has been compared with the popular clustering algorithms like K-means, Fuzzy C Mean, Possiblistic Fuzzy C Means and Linde Buzo Gray algorithms. The results obtained demonstrated that for the given number of management zones the median strange point algorithm outputs are at par; in some cases superior than the standard algorithms. The proposed experimentation is carried out on the Sugarcane (Saccharum Officinarum) datastet of a small farm of size 2.83ha (7 acres) in Kanhegaon village, Ahmednagar (Maharashtra), India.

K-means, Fuzzy C Mean, Possiblistic Fuzzy C Means, LBG, Management zones

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Site-specific management guidelines. Atlanta, USA: Potash and Phosphate
Institute, 1999. (article)

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Maertens; M.R. Maleki; H. Ramon; J. De Baerdemaeker,“ Management Zones based on
Correlation between Soil Compaction, Yield and Crop Data”, Biosystems
Engineering, 92 (4), pp.419–428, 2005.

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[5]   F. Guastaferro, A,  Castrignano`, D. De Benedetto, D. Sollitto,
A. Troccoli, B. Cafarelli, “A comparison of different algorithms for the
delineation of management zones”, Precision Agric 11:pp.600–620, 2010.

[6]   G. Rub, R. Kruse, M. Schneider, “A clustering
approach for management zone delineation in precision agriculture”, 2010.

[7]   K. Aggelopooulou, A. Castrignanò, T. Gemtos,
D. De Benedetto, “Delineation of management zones in an apple orchard in Greece
using a multivariate approach”, Computers and Electronics in Agriculture 90
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Ciferri, “A Cluster-Based Approach to Support the Delineation of Management
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[10] N.R. Kitchen, K.A. Sudduth, D.B. Myers, S.T.
Drummond and S.Y. Hong, “Delineating productivity zones on claypan soil fields
using apparent soil electrical conductivity”, Computer and Electronics in
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[11] R. T. Santos, J. P. Molin, A. M. Saraiva, “A
Reference Process for Management Zone Delineation”, Sustainable Agriculture
through ICT Innovation, June 2013.

[12] Ye, X.F., Zhang, X.Y., Cui, S.Y., Yu, Q.W.,
Wang, Y.Y., Liu, G.S., Effect of different potassium application rate on the
potassium contents in tobacco leaves and soil. J. Henan Agric. Univ. 40,
pp.473–476 (in Chinese), 2006.

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A.R., Delineation of site specific nutrient management zones for a paddy
cultivated area based on soil fertility using fuzzy clustering. Geoderma
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[14] Xin-Zhang,W., Guo-Shun, L., Hong-Chao, H.,
Zhen-Hai,W., Qing-Hua, L., Xu-Feng, L., Wai-Hang, H., Yan-Tao, L.,
Determination of management zones for a tobacco field based on soil fertility.
Comput. Electron. Agric. 65, pp.168–175, 2009.

[15] R. Tripathi, A.K. Nayaka, Mohd. Shahid ,B. Lal,
P.Gautama, R. Raja, S. Mohanty, A. Kumar, B.B. Panda, R.N. Sahoo, “Delineation
of soil management zones for a rice cultivated area in eastern India using
fuzzy clustering” Catena 133, pp.128–136, 2015.

[16] Mokaya, “Future of Precision Agriculture in
India using Machine learning and Artificial Intelligence”, IJCSE, Vol.7, Issue
2, pp. 1020-1023, 2019.

[17] R R 
Priyanka , M Mahesh, S. S. Pallavi , G. Jayapala, M. R. Pooja,”Crop
Protection by an alert Based System using Deep Learning Concept”, IJSRCSE,
Vol.6, Issue.6, pp.47-49, 2018.

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, Issue.5 , pp.8-11, Oct-2016 

[19] P. Janrao, H. Palivela, “Management zone
delineation in Precision agriculture using data mining: A Review”, IEEE
Sponsored 2nd International Conference on Innovations in Information Embedded
and Communication Systems ICIIECS’2015, Vol. 5, pp.839-845, 2015.

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P. P.Janrao, D.S. Mishra, V. A. Bharadi, "A Median Strange Point algorithm for Delineation of Agricultural Management Zones," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.7-12, 2020.

Bekasi`s land and building tax revenue have not yet reached its annual target. This is caused by a bureaucratic complicated warning and payments. The information was obtained from the official body of the city of Bekasi in the form of tax revenue data and interviews. This research contains an official web development feature design for Bekasi city. These features are online land and building tax payments and visualization of tax revenue with Geographic Information Systems. The design was made using the Rapid Application Development method and using UML tools and tested with COBIT 4.1. Activity diagrams and use-case diagrams are used to determine user behavior. The prototype was made with Adobe XD. The COBIT domain used is Delivery and Support, namely DS1, DS4 and DS7. The assessment results show that with the prototype, respondents can understand the information provided, know the stages of payment, and agree that development is needed.

RAD, Prototype, COBIT

[1]   B. Sobandi, “Strategi Optimalisasi Pendapatan
Asli Daerah (PAD): Kasus Kota Banjarmasin”, LEMHANNAS RI, ISSN 08539340, 2004.

[2]   A. Muhtarom, “Analisis PAD Terhadap
Kesejahteraan Masyarakat Kabupaten Lamongan Periode 2010-2015”, EKBIS Journal,
Vol.1, Issue. 8, 2015.

[3]   M.A. Manuhutu, L.J. Uktolseja, “Design and
Implementation of Online Students’ Complaint (Case Study of English Study
Program at Victory University, Sorong)”, -IJCSE, Vol-6, Issue. 1, pp. 228-232,
2018.

[4]   N. Bharanikumar, P. Dhanalakshmi, “Survey on
Machine Learning Algorithms for Classification and Prediction of Land Use
Changes Using GIS”, International Journal of Computer Sciences and Engineering,
Vol-7, Issue. 4, pp.351, 2019.

[5]   R. Rendra, Wasilah, “Use of Cobit Framework
4.1 Method in Auditing The Academic Information System (Siakad) in Intan
Lampung Raden IAIN”, TIM Darmajaya Journal, Vol.1, Issue.1, pp.83-91, 2015

[6]   D. Kennedy. “Introducing Geographic
Information Systems with Arc-GIS: A Workbook Approach to Learning GIS, 3rd
Edition”, Wiley, Chapter 9, 2013. ISBN no 9781118159804.

[7]   G. Saxena, “Data Warehouse Designing:
Dimensional Modelling and E-R Modelling”, IJEI, Vol.3, Issue.9, pp.28-34, 2014.

[8]   K.E. Kendall, J.E. Kendall, “SYSTEM ANALYSIS
AND DESIGN”, Prentice Hall, 2005, ISBN no 0130415715.

[9]   A. Ambarwati, R. Rusady. “Analisis
Implementasi Teknologi Informasi pada Domain Deliver and Support di PT. RDPI”,
INFORM Journal, Vol.2, Issue.2, 2017.

M. Fauzan, Bertalya, "Web Development Prototype on Land and Building Tax Revenue Features (Case Study: Bekasi City)," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.13-17, 2020.

Diabetes mellitus is one of the world’s fast-growing diseases. Differentiation is among the most important decision-making approaches in many real-world problems. In this work, the main objective is to classify the diabetic patient’s data into various levels based upon the values. This will help to assist the required dose which should be provided to the patients through an automatic insulin pump.  The efficiency of the different classifiers is measured to assess the reliability of the classification. In this analysis, four common algorithms for machine learning, namely Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression, Random forest, and decision tree, for the estimation of diabetic mellitus on data from the adult population. Based on the comparison of performance parameters like precision, recall, F1-score, and accuracy the algorithms are ranked and selected the best among all. The accuracy value of Logistic Regression is the highest among the other algorithm, therefore Logistic Regression performs best with the patient data in forecasting diabetes mellitus.

Diabetes mellitus, Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression, Random forest, decision tree

[1]   B. Sobandi, “Strategi Optimalisasi Pendapatan
Asli Daerah (PAD): Kasus Kota Banjarmasin”, LEMHANNAS RI, ISSN 08539340, 2004.

[2]   A. Muhtarom, “Analisis PAD Terhadap
Kesejahteraan Masyarakat Kabupaten Lamongan Periode 2010-2015”, EKBIS Journal,
Vol.1, Issue. 8, 2015.

[3]   M.A. Manuhutu, L.J. Uktolseja, “Design and
Implementation of Online Students’ Complaint (Case Study of English Study
Program at Victory University, Sorong)”, -IJCSE, Vol-6, Issue. 1, pp. 228-232,
2018.

[4]   N. Bharanikumar, P. Dhanalakshmi, “Survey on
Machine Learning Algorithms for Classification and Prediction of Land Use
Changes Using GIS”, International Journal of Computer Sciences and Engineering,
Vol-7, Issue. 4, pp.351, 2019.

[5]   R. Rendra, Wasilah, “Use of Cobit Framework
4.1 Method in Auditing The Academic Information System (Siakad) in Intan
Lampung Raden IAIN”, TIM Darmajaya Journal, Vol.1, Issue.1, pp.83-91, 2015

[6]   D. Kennedy. “Introducing Geographic
Information Systems with Arc-GIS: A Workbook Approach to Learning GIS, 3rd
Edition”, Wiley, Chapter 9, 2013. ISBN no 9781118159804.

[7]   G. Saxena, “Data Warehouse Designing:
Dimensional Modelling and E-R Modelling”, IJEI, Vol.3, Issue.9, pp.28-34, 2014.

[8]   K.E. Kendall, J.E. Kendall, “SYSTEM ANALYSIS
AND DESIGN”, Prentice Hall, 2005, ISBN no 0130415715.

[9]   A. Ambarwati, R. Rusady. “Analisis
Implementasi Teknologi Informasi pada Domain Deliver and Support di PT. RDPI”,
INFORM Journal, Vol.2, Issue.2, 2017.

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& Hernando, M. E. (2010). Artificial neural network algorithm for online
glucose prediction from continuous glucose monitoring. Diabetes technology
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B. Vinothkumar, M. Ramaswami, "A Novel Prediction of Diabetes by Automatic Insulin Therapy Using Machine Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.18-23, 2020.

Data science is the occupation of future, because organizations that are unable to use (big) data in a smart way will not survive. Process mining is a rising area that fills the gap between business process management techniques and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data and process models (hand-made or discovered automatically).It can be applied to any type of operational processes.Business process management is a top down approach. BPM starts by designing your process in high level model. Then you configure your system for managing and controlling the designed process. This system then coordinates work between employees and other resources in organization such that the organization is able to achieve the planned process. On the other end process mining analyzes process in a bottom-up fashion. That is we do not need to have model of process. Process mining uses the history data which is present in IT systems in the form of event data. Using this event data process mining generate process models as per the generated models organizations can  take further steps to improve the models that are generated. As a result an organization cannot change the data but it can change the process in which the data is generated and hence work to meet the goals of organization. This paper gives an abstract view of process mining- algorithms used, applications, scope of process mining in diverse disciplines, research issues of Spaghetti process mining with concept drift.

Business intelligence,business process management,operational processes, process mining, event data,process models,concept drift,Spaghetti process mining.

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Martin Prodel, Vincent Augusto, “Optimal processmining for large complex event
logs”,2018 IEEE transactions on automation science and engineering.

[2]
Na Liu, Jiwei Huang, LizhenCui, “A Framework for online process Concept Drift
Detection from Event Streams”, In  the
proceedings of 2018 IEEE International Conference on Services
Computing,pp.105-113.

[3]
Manoj Kumar,Likewin Thomas,Annappa, “Distilling Lasanga from Spaghetti
Processes”, In Proceedings of ACM Woodstock conference, Hong Kong, Hong Kong,
March 25-27, 2017 (ISMSI ’17),

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Wil M.P. vander Aalst ,Verlag Berlin Heidelberg, “Process Mining Analyzing
Sphagetti process”2016 Springer pp.411-428

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Literature Review” , Research Gate Conference paper Dec18.

[7]
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On Process Mining for Healthcare Processes” ,Appl. Sci. 2018,

[8]
Josef K.J. Martens, “Professional use of Process Mining for analyzing Business
Processes”,2013-CEUR workshop proceedings .

 [9] Ufuk Celik , “Process Mining Tools Comparison”,
Online Academic Journal of Information Technology 2018

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N. Swapna, L. Ramaparvathy, "Study On Spaghetti Process Mining with Concept Drift," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.24-27, 2020.

It is very difficult to remove the shadow from video frames or an image, so we need to find the solution for this. Here we did survey for the required information. Due to the excessive-order textural material within the video frame directly removed the intensity area of the shadow frame is difficult. There are various filters for image segmentation, here we used a mean-shift filter to remove a noise and smoothening of video frame. A region growing algorithm is used for the shadow detection through the user. Using 3D modelling and inpainting, an intensity surface of illumination in the shadow region can be received primarily based on that similar to the equal texture inside the non-shadow one. In comparison to the alternative strategies, this is a user-assisted method that solves a shadow detection problem and applies to the shadow, consisting of various types of textures. In future, when we apply this system for video, this process is well suitable for the less duration video because, for each frame the user gives some important information to the system

Mean-shift filter, Region Growing algorithm , Image inpainting, Shadow removal

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“Editing soft shadows in a digital photograph,” IEEE Comput. Graph., Appl.,
vol. 27, no. 2, pp. 23–31, Mar./Apr. 2007

[5]   E. Arbel and H. Hel-Or, “Shadow removal using
intensity surfaces and texture anchor points,” IEEE Trans. Pattern Anal. Mach.
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L. Yuan, and X. F. Hu, “An improved shadow removal algorithm based on gradient
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[11] A. Mohan, J. Tumblin, and P. Choudhury,
“Editing soft shadows in a digital photograph,” IEEE Comput. Graph. Appl., vol.
27, no. 2, pp. 23–31, Mar. /Apr. 2007.

[12] Kai He, Rui Zhen, Jiaxing Yan, and Yunfeng Ge,”
Single-Image Shadow Removal Using 3D Intensity Surface Modeling” IEEE
Transactions On Image Processing, Vol. 26, No. 12, December 2017.

[13] Huifang Li,Liangpei Zhang,“An Adaptive Nonlocal
Regularized Shadow Removal Method for Aerial Remote Sensing Images” IEEE Trans.
Geosci. Remote Sensing, Vol. 52, No. 1, January 2014

[14] R.K. Sasi, V.K. Govindan, Fuzzy split and merge
for shadow detection, published in Egyptian Informatics Journal, Vol. 16,
pp.29-35, 2015.

[15] Roshani L Jain, Lubdha M Bendale and Gaytri D.
Patil, “Image Enhancement using different Techniques”IJSRCSE Vol.06, Special
Issue.01 , pp.73-76, Jan. 2018

[16] Palak kumar, Vineet Saini  “An Efficient Image Sharpening Filter for
Enhancing Edge Detection Techniques for 2D, High Definition and Linearly
Blurred Images”vol.2,Issue.1,Feb.2014.

M.S. Kalas, B.D. Sonawane, "Design of Enhanced Method for Detection and Removal a Shadow from Video Frames," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.28-32, 2020.

Wireless communications is the fastest growing segment of the communication industry. The most common used wireless communication is mobile communication. But, there are many technical challenges that must be overcome. In A signal transmitted on a wireless channel is subject to Interference, Fading, Propagation path loss, Shadowing etc. There is always a greater order for capacity with the high quality service. In this situation, OFDM is well defined technique, which is a very much suitable option for high band width data transmission, by converting the wideband signal into narrow band signals for transmission. The transmission of these individual narrow band signals are executed with orthogonal carrier. In this dissertation, the performance of transmission mode are evaluated by Bit Error Rate versus the Signal to Noise Ratio under frequently used Rayleigh channel modes,. In order to investigate, first we derive the mathematical modeling for bit error rate and signal to noise ratio of OFDM over Rayleigh then, OFDM is design. now we have assumed Rayleigh fading channel as noise channel and also built BPSK, QPSK and QAM modulation technique. OFDM transmitters and receivers are implemented here using IFFT and FFT of size 64 with 52 sub carriers to convert the spectra to time domain & vice versa and also measure peak to average power ratio in different modulation scheme.

PAPR, Digital Audio Broadcasting (DAB) ,(OFDM) orthogonal frequency division multiplexing ,MIMO ,Low Density Parity check(LDPC) and Complementary cumulative distribution function(CCDF) ,Digital amplitude modulation (DAM), ACLR,OOB

[1]  Vipin Kumar, Dr.Praveen, Anupama“Performance
Analysis of MIMO OFDM system using BPSK &QPSK modulation techniques under
Rayleigh Fading Channel” IJARCET 2016.

[2]   ArunAgarwal, KabitaAgarwal “Performance evaluation of
OFDM based WiMAX (IEEE 802.16d) system under diverse channel conditions” IEEE
Jan 2015

[3]   
Tareq Y. Al-Naffouri ,Ahmed A. Quadeer “Impulse Noise
Estimation an Removal for OFDM Systems” IEEE Transactions on communication  (Volume:62 ,  Issue: 3 ) February 2014

[4] 
Parul Wadhwa , Gaurav Gupta, “BER Analysis & Comparison
of Different Equalization Techniques for MIMO-OFDM System”, International
Journal of Advanced Research in Computer Science and Software Engineering,
(IJARCSSE), Volume 3, Issue 6, June 2013.

[5]  Vishal Anand, Sharmeele Thanagjam, Sukhjeet
Kaur, “Analysis of OFDM BER and PAPR using TURBO CODE”, IJCSNS International
Journal of Computer Science and Network Security, VOL.12 No.3, March 2012.

[6]   Ligata, Haris Gacanin, Fumiyuki Adachi, Miha
Smolnikar, and Mihael Mohorcic, “Bit Error Rate Analysis for an OFDM System
with Channel Estimation in a Nonlinear and Frequency Selective Fading Channel”,  EURASIP Journal on Wireless Communications
and Networking, 2011.

[7]    Vivek K. Dwivedi and G. Singh, “An
Efficient BER Analysis of OFDM Systems with ICI Conjugate Cancellation Method”,
Progress In Electromagnetics Research Symposium, Cambridge, USA, July 2-6,
2008.

[8]  S. Coleri, M. Ergen, A. Puri, and A. Bahai,
“Channel estimation techniques based on pilot arrangement in OFDM systems”,
IEEE Transactions on Broadcasting, vol. 48, pp. 223-229, 2002.

[9]  Y. Jung, Y. Tak, J. Kim, J. Park, D. Kim, and
H. Park, “Efficient FFT algorithm for OFDM modulation”,  Proceedings of IEEE Region 10, International
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2001.

[10] L. J.
Cimini Jr, Ye, L., “Orthogonal Frequency Division Multiplexing for Wireless Ch
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[11]  S. Weinstein and P. Ebert, “Data transmission
by frequency division multiplexing using the discrete fourier transform”, IEEE
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[14]   M. L. Doelz, E. T. Heald, and D. L. Martin,
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Milind Sharma, Shiv Kumar, Anlit Navlakha, "Enhanced Performance Analysis of OFDM, Measuring Bit Error Rate and Peak to Average Power Ratio using Different Modulation," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.34-40, 2020.

 In MIMO-OFDMA (multiple input multiple output-orthogonal frequency division multiplexing access) when signals are transmitted from the transmitter to receiver different types of error detection techniques are used for calculating the BER (bit error rate), which is an important factor in characterizing the data channel. Among various modulation schemes techniques, MMSE (minimum mean square error) is a versatile technique in which it is not necessary to calculate, explicit the posterior probability density function. In MMSE, we calculate the BER on previously known parameters and no need to assume random variables as a result among all estimators and the accuracy rate is higher. MMSE to reduce BER uses “MINIMUM MEAN SQUARE ALGORITHM” that is MMSE algorithm which is pre-owned to minimize the error and achieved the optimal performance at a cost of high computational complexities. 

MIMO-OFDM, MMSE, ML, ZF, BER

[1]     Purnima k Sharma, T. Vijaysai, “Bit error
rate analysis of pre-coding techniques in different MIMO systems”, proceedings
of the 2^nd international conference on electronics, communication
and aerospace technology (ICECA 2018) IEEE Explore ISBN: 978-1-5386-0965-1, pp.
1144-1147.

[2]     Madalina –Georgiana Berceanu, Carmen Voicu,
Simona Halunga, “the performance of an uplink large scale MIMO –SIC detector”,
International conference on military communications and information systems
(ICMCIS), 2019, pp. 1-4.

[3]     Shubangi R. Chaudhary, Madhavi Pardeep Thombre,
“BER performance analysis of MIMO –OFDM system using different equalization
techniques”, IEEE International conference on advanced communication control
and computing technologies (ICACCCT), 2014, pp. 637-677.

[4]     Zenitha Rehman and H.M.Savitha, “BER
performance comparison of MIMO systems using OSTBC with ZF and ML
decoding”,  ICTACT Journal on
communication technology, December 2014, volume:5, ISSUE:04, pp.1030-1034

[5]     Brijesh Kumar Yadav, Rabinder Kumar Singh ,
and Sovan Mohanty, “performance analysis of efficient and low complexity
MIMO-OFDM using STBC and V-BLAST”, 
International conference on emerging trends in electrical, electronics
and sustainable energy systems (ICETEESES-16), IEEE 2016, PP. 204-207.

[6]     Superaja Eduru, Nakkeran Rangaswamy, “BER
analysis of massive MIMO system under correlated Rayleigh fading channel”,
IEEE-43488, 9^th ICCNT 2018 IISC Benguluru, India.

[7]     Mr.Kanchan Sharma, Ambika Verma, Jasneet
kaur, and Trishla, “performance evaluation of fountain codes for MIMO-OFDM
system over Rican faded channel”,2014 international conference on medical
imagining, m-health and emerging communication systems (Medcom), pp. 319-324.

[8]     Kamaljit Kaur gill and Kiranpreet Kaur, “comparative
analysis of ZF and MMSE detections for Nakagami –m faded MIMO channels”, IEEE
INDICON 2015, pp.1-5.

[9]     Himankashi Grover, Reetu Pathania,
“performance analysis of MMSE equalizer and time-frequency block code for
MIMO-OFDM under single-RF system”, proceeding of 2^nd   international conference on inventive
communication and computational technologies (ICICCCT)   2018, pp. 1277-1280.

[10]  Lusekelo Kibona, liujain & liuying Zhaung,
“BER analysis using Zero –forcing linear pre-coding scheme for massive MIMO
under imperfect channel state information”, international journal of electronics,
2019.

[11]  M.D. Renzo. And H.Hass. H, “Bit error
probability of SM-MIMO over generalized fading channel”, IEEE Transactions on
vehicular Technology, vol. 61, no.3, pp.1124-1144, March 2012.

[12]  Oktavia Kartika Sari, I Gede Puja Asthwa,
“performance analysis of MIMO-OFDM systems with SIC detection based on Single
–RF with convolution code”, international electronics Symposium (IES) 2016, PP.
289-294, 2016.

[13]  Jabbar, s.; li, y., “Analysis and Evaluation
of performance gains and the trade-off for massive MIMO Systems” (appl. Sci.),
pp. 1-30, September 2016.

[14]  J. Choi and H.Naguyen, “SIC-Based detection
with list and lattice reduction for MIMO channels, “In IEEE Transactions on
vehicular technology, vol.58, no. 7, pp.3786-3790, Sept. 2009.

[15]  M.Berceanu, C.voice and S.Hangula,
“performance analysis of an uplink massive MU-MIMO OFDM based system”, 2018
international conference on communications (COMM), Bucharest, 2018, pp.335-339.

[16]  M.Brceanu, C. Voicu and S. Hangula, “uplink
massive MU-MIMO OFDM based system with LDPC coding-simulation and
performances”, 9^th   edition
of International conference Advanced topics in optoelectronics, microcontrollers,
and nanotechnologies, 2018, Constanta.

[17]  W.Li, C.Zhang, X.Duan, S.Jia, et al.,
"Performance evaluation and analysis on group mobility of group mobility
of mobile-ready for LTE-Advanced systems”, in proc. IEEE VTC 2012-fall.

[18]  Purnima k. Sharma and Dinesh Sharma, “mobile
wireless technology is boon or curse”, Indian journal of science and
technology, vol. 9 (40), DOI:10.17485/ijst/2016/v9i40/84293, October 2016.

[19]      Md. Hashemali Khan, KM Cho, Moon Ho Lee*,
and Jin-Gyunchun I g, “A Simple bock diagonal pre-coding for multiuser MIMO
broadcast channels”, EURASIP Journal on wireless communication and networking: 95,
2014.

[20]  Suresh Kumar Jindal, “MIMO system for
military communication /applications”, International journal of engineering
research and applications”, ISSN/; 2248-9622, VOL. 6, issue 3, (part -4) March 2016,
pp. 22-33.

[21]  Eman al-Khadar, Shereen, “pre-coding and
post coding for capacity enhancement in MIMO systems” in Procedia computer
science, volume-52, pp. 326-333, 2015.

[22]  Er.Navjot Singh, Er. Vinod Kumar linear and
non-linear decoding techniques in multi-user MIMO systems” A review vol. 4,
issue-5, May 2015.

[23]  Parul Wadhwa, Gaurav Gupta, “BER Analysis
& Comparison of different decoding techniques for MIMO-OFDM system”
International Journal of Advanced Research in computer science and software
engineering volume 3, ISSUE 6, ISSN:2277 128X, June 2013.

[24]  L.T.Berger, Andreas Scwager, Daniel
Schneider and Pascal Pagani, “MIMO Power line communications: Narrow and
Broadband Standards, EMC, and Advanced Processing”, Devices, circuits, and
systems 2014.

[25]  Bhasker Gupta and Davinder S. Saini, “BER
performance improvement in MIMO systems”, 2^nd IEEE International
Conference on Parallel, Distributed and grid computing, 2012.

[26]  K. Punia, E.M. George, K.V. Babu, and G.R.
Reddy, “Signal Detection for spatially multiplexed multi-input multi-output(MIMO)
systems”, in Proe. International conference on communication and signal
processing, April 2014, pp.612-616.

[27]  A.H. Mehana, and A. Noratinia, “Diversity of
MMSE MIMO Receivers”, IEEE Transactions on information theory, vol. 58, no. 11,
pp. 6788-6805, November 2012.

[28]  Zhou, y., Shen, B., Wang, Q., & Choi,
D.(2017), “ A New joint zero-forcing beam forming algorithm for massive MIMO
systems”, IEEE Transactions on wireless communications,15(3),2245-2261.

[29]  Silva, S., 
Baduge, G. A. A., Ardakani, M.,& STellambura, c.(2018), “Performance
analysis of massive MIMO two- way relay network with pilot contamination,
imperfect CSI and antenna correlation”, IEEE Transactions on vehicular
technology, 67(6),  4831-4842.

[30]  Shehab W. A., & Al-qudah, Z. (2017),
“singular value decomposition: principles and applications in multiple input
multiple output communication system”, Intl. J. Computer Network Communication,
9(1), pp.  13-21.

Jyoti, Vikas Nandal, "BER Performance Comparison of Various Modulation Schemes using MMSE on MIMO System over Various Fading Channel," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.41-48, 2020.

Agriculture sector in India is moving back day by day which affects the production capacity of ecosystem. Agriculture has usually been treated as an instinctive space with insight passed down from one generation to another. But today’s problems like the climate change, reduction of feasible farmland, reduction of water resources, and the loss of productivity due to the occurrence of extreme weather events are more complex and vital in the present nature. The United Nations estimates that the global population will reach 9.8 billion by 2050 that is a 2.2 billion increase from now [1]. This means that there is a need to improve our crop production significantly to supply to the rising number of people. One of the important things that are associated with present agriculture is the capability to forecast the events that will produce a desired outcome. Farmers need proper information throughout the entire farming cycle to achieve the goals. The required information is sprinkled in various places which includes actual information such as market prices and current production level along with the existing primary crop knowledge. The world around agriculture requires automation by replacing manual procedures with the expansion of technology, because it is energy efficient and takes minimal man power. Some farmers simply cannot increase their land to cultivate more crops, in that situation there is a need for technology to make better use of the available space in an efficient manner. This situation leads for technological innovation, to face the challenges like extreme weather conditions, rising climate change, and farming’s environmental impact. To meet these increasing needs, agriculture has to revolve to new technology. Now a day’s there is rapid enhancement in technologies, different tools and techniques are available in agriculture sector. To improve efficiency, productivity, global market and to reduce human intervention, time and cost there is a need to divert towards new technologies named IoT, Big Data and AI – The Three Digital Pillars of every Industry. This paper emphasizes on the analysis of the agriculture data and finding optimal parameters to maximize the crop production using technologies, examines the challenges, applications and opportunities of these technologies and concludes that these technologies will lead to relevant analysis at every stage of the agricultural value chain that leads to smart agriculture.

Internet of Things, Big Data, AI, Smart Farming.

[1]     World Population Projected To Reach 9.8 Billion In 2050, And 11.2
Billion In 2100. Accessed: Apr. 18, 2019. [Online].

[2]    
Special
Section On New Technologies For Smart Farming 4.0: Research Challenges And
Opportunities, Received July 7, 2019, Internet-Of-Things (IOT)-Based Smart
Agriculture: Toward Making The Fields Talk, Corresponding Author: Muhammad Ayaz
.

[3]     Iot for All, Iot Applications In Agriculture, January 3, 2020.

[4]     Data Flair, Iot Applications In Agriculture – 4 Best Benefits Of Iot In
Agriculture, By Dataflair Team · Updated · September 15, 2018.

[5]     Telit, Crop and Live Stack Management, Support Live Stock Health
Through Real Time Monitoring.

[6]    
Seriun,
Big Data, The Internet Of Things(Iot), And Artificial Intelligence(Ai)

[7]    
M. Ayaz Et Al.: Iot-Based Smart Agriculture: Toward
Making The Fields Talk.

[8]     The Many Practical Uses Of Big Data In Agriculture, September 17th
2018.

[9]     Intellias Blog Post, How To Encourage Formers To Use Big Data Analytics
In Agriculture.

[10]  Eleks Blog Post, How Is Big Data Being Used?.

[11]  Big Data And Agriculture :A Complete Guide

[12]   Agricultural
Systems, Volume 153, May 2017, Pages 69-80.

[13]  The Role Of Artificial Intelligence In Agriculture Sector, Jyoti
Gupta October 11, 2019,
Advantage Of Implementing Ai In Agriculture.

[14]  Online Sciences: Applications Of Artificial Intelligence In
Agriculture, Agricultural Robots, Drones & Plantix App, By Heba Soffar · Published July 9, 2019.

[15]  E Agriculture, Food And Agriculture Organization Of The United Nations,
Can Artificial Intelligence Help Improve Agricultural Productivity?

[16]  Ai For Weather Forecasting – In Retail, Agriculture, Disaster
Prediction, And More Last Updated On May 20, 2019, Published By Jon Walker.

[17]  Ai In Agriculture – Present Applications And Impact, Last Updated On
November 21, 2019, Published By Kumba
Sennaar.

[18]   Team Talk, Iot, Big Data And Ai
– The Three Digital Pillars Of Every Industry, Published On July 16, 2019.

[19]  World agriculture, The Benefits of Internet of Things (IoT) for Smart
Agriculture

[20]  Slide share, aws+intel-smart-greenhouse-demo,

[21]  BRAEMAC,IOT and lorawan modernize livestock monitoring

[22]  L. Zhang, I. K. Dabipi, andW. L. Brown, ``Internet of Things
applications for agriculture,`` in Internet of Things A to Z: Technologies and
Applications, Q. Hassan, Ed., 2018.

[23]  E. Sisinni, A. Saifullah, S. Han, U. Jennehag, and M. Gidlund,
``Industrial Internet of Things: Challenges, opportunities, and directions,``
IEEE Trans. Ind. Informat., vol. 14, no. 11, pp. 4724_4734, Nov. 2018.

[24]  J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao,``A survey on
Internet of things: Architecture, enabling technologies, security and privacy,
and applications,`` IEEE Internet Things J., vol. 4, no. 5,pp. 1125_1142, Oct.
2017.

[25]    X. Hi, X. An, Q. Zhao, H. Liu, L. Xia, X. Sun,
and Y. Guo, ``State- of-the art Internet of Things in protected agriculture,``
Sensors, vol. 19, no. 8,p. 1833, 2019.

[26]  O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. N.Hindia, ``An
overview of Internet of Things (IoT) and data analytics in agriculture: Bene_ts
and challenges,`` IEEE Internet Things J., vol. 5, no. 5, pp. 3758_3773, Oct.
2018.

[27]   S. Kim, M. Lee, and C. Shin,
``IoT-based strawberry disease Prediction system for smart farming,`` Sensors,
vol. 18, no. 11, p. 4051, 2018

[28]   Dangermond, J. (2018). “The
Emergence of a Geospatial Cloud and the Continuing Evolution of Cloud
Computing”, ArcNews, spring 2019, Vol. 41, No. 2.

[29]  International Journal of Scientific Research in Review Paper Computer
Science and Engineering- Deep Learning Techniques, Vol.8, Issue.1, pp.105-109,
February (2020).

Kannepalli Subhadra, "The Role of Emerging IT Technologies in Agriculture," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.49-57, 2020.

Earthquake is sudden shaking of the ground surface caused by the movement of seismic waves through Earth’s rocks. Earthquakes are one of the major disasters and their unpredictability causes even more destruction in terms of human life and financial losses. The aim of the project is to predict the chances of earthquake using wireless sensor network data and machine learning and to alert people before the disaster occurs and save their lives.  In the project a simpler way of detecting the occurrence of earthquake has been introduced. It is based on collecting WSN data using the API’s and Machine learning algorithms where weather information API is used to fetch live weather details. The collected live weather data and the previous details of the weather in a particular place are passed to Machine learning algorithms i.e. SVM, KNN, Random Forest, Decision tree and the algorithm which gives more accuracy is chosen and is applied on it to predict the current chances of disaster occurrence. If there is a chance of occurrence of the disaster (Earthquake) then an alert message is sent to the concerned authority to create awareness among people.

WSN, SVM, KNN, Decision tree

[1]   R S Prasanna kumar, M S
Shazia Anjum, “Landslide detection using wireless sensor network in disaster
monitoring” , International Journal of Innovative Research in Science ,volume
8, issue 4,pp. 4529,2019.

[2]   V Maneesha. Ramesh,
“Real-time Wireless Sensor Network for Landslide Detection”, IEEE Third
International Conference on Sensor Technologies and Applications, pp. 405-409.
2009.

[3]   J  Reyes, A Morales-Esteban, F.
Martı´nez-A´lvarez,”Neural networks to predict earthquakes in Chile. Applied
Soft Computing”, 2013.

[4]   KM Asim, M Awais, F
Martı´nez–A´lvarez, T Iqbal,“Seismic activity prediction using computational
intelligence techniques in northern Pakistan. ActaGeophysica”, pp.919,2017.

[5]   DR Nayak, A Mahapatra, Mishra
, “A survey on rainfall prediction using artificial neural
network,.International Journal of Computer Applications”,pp.72,2013.

[6]   Mr. S AnandBhosle1,Mr
M.Laxmikant.Gavhane, “Forest Disaster management with Wireless Sensor Network”
International Conference on Electrical, Electronics, and Optimization
Techniques (ICEEOT) , 2016.

[7]   Joko Windarto, “Flood Early
Warning System Developed at Garang River semarang using Information Technology
based SMS and WEB”,   International
Journal of Geomatics and Geoscience, vol 
no.1,pp.14-26, 2010.

[8]   MING Xiaodong XU, Wei LIU
Baoyin, etc, “An Overview of the Progress on Multi-Risk Assessment”, Journal
of  Catastrophology, Vol 28(1),
pp.126-132, 2013.

[9]   Rahul R. Biswas, Tripti R.
Biswas, “Modeling Seismic Effects on a Stormwater Network and Post-earthquake
Recovery,” International Journal of Computer Sciences and Engineering, Vol.5,
Issue.9, pp.55-61, 2017.

[10] Avinash Vibhute, Vaibhav
Shinde, Amar Paranjape, “Structural Integrity Assessment of Genset Structure
for Earthquake Loads, International Journal of Computer Sciences and
Engineering,” Vol.7, Issue.6, pp.105-109, 2019.

[11] Rosy Mishra, Rajaram Meher,
Pratibha Bhoi, Amisha Mohanty, “Cyclone and Earthquake Recognition and
Estimation using HSV Colour Segmentation and Clustering”, IJSRNSC, Vol.7,
Issue.4, pp.8-20, Aug-2019

[12] R.R Priyanka, M Mahesh,S.S
Pallavi, Jayapala G, M.R Pooja, “Crop Protection by an alert Based System using
Deep Learning Concept”Isroset,Vol.6 , Issue.6 , pp.47-49, Dec-2018.

Shafiya S, RS Prasanna Kumar, "Earthquake Prediction using WSN Data and Machine Learning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.58-60, 2020.