An efficient hydro-crop growth prediction system for nutrient analysis using machine learning algorithm

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6681~6690
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6681-6690  6681
Journal homepage: http://ijece.iaescore.com
An efficient hydro-crop growth prediction system for nutrient
analysis using machine learning algorithm
Chandana Chikkasiddaiah1,2
, Parthasarathy Govindaswamy2
, Mallikarjunaswamy Srikantaswamy3
1
Master of Computer Applications, JSS Academy of Technical Education, Bengaluru, India
2
School of Computing and Information Technology, REVA University, Bengaluru, India
3
Electronics and Communication Engineering, JSS Academy of Technical Education, Bengaluru, India
Article Info ABSTRACT
Article history:
Received Jan 28, 2023
Revised Mar 12, 2023
Accepted Apr 7, 2023
The hydro nutrient management (HNM) for crop yield is effectively improved
using proposed system. A hydro-crop growth prediction system (HCGPS) is
designed using machine learning. The reconfigurable nutrients uptake crop
yield prediction rate is enhanced. This proposed HCGPS is used to predict the
crop yield by considering input parameters such as nutrient index (NI), electric
conductivity limit (ECL), ion concentration factors (ICF) and dry weight of
the crop and crop yield rate (CYR) to analyze the positive and negative
correlation with crop growth. The proposed system is used to find correlation
Index of input and output parameters to determine the prediction rate of crop
yield. The proposed design improves smart prediction rate and efficiency of
crop growth rate with optimal utilization of input variables. This proposed
HCGPS is very helpful to achieve good quality yield with optimal utilization
of input parameters.
Keywords:
Crop yield rate
Dry weight
Electric conductivity limit
Hydro nutrient management
Nutrient index
This is an open access article under the CC BY-SA license.
Corresponding Author:
Chandana Chikkasiddaiah
Master of Computer Applications, JSS Academy of Technical Education
Bengaluru, Karnataka, India
Email: punyachandu@gmail.com
1. INTRODUCTION
Hydro nutrient management is a soilless technique to nurture yields in water. Such zero-soil farming
way characterizes kind of an outstanding chance for the cultivation sphere, particularly in fields connecting
with trials like uncontrollable soil deprivation and controlled water bases. Additionally, such farming practice
proves outstanding consequences concerning an atmosphere and user responsive agriculture. It is similarly a
consistent tool for the forthcoming trials in food security. Numerous areas of the world which face trials like
volatile weather forms, extreme heat, condensed space obtainability, are captivating hydro nutrient
management (HNM) as a substitute method to agriculture and a probable answer for such difficulties. The
global marketable HNM business has increased four-five-fold in the preceding 10 years and is presently
estimated amid 21k and 26k hectares with a farm gate value of US $5.9 to $7.9 billion. As a consequence,
agriculture perceives are quickly shifting to smarter and accuracy agriculture observes for enhanced crop
harvests and commercial gains. With the novel age of big data, approaches like machine learning (ML) and
artificial intelligence (AI) available, data can be used to analyze, yield patterns and make forecasts [1], [2].
Depending on the above concerns investigators have advised and exasperated to overcome such
breaches by uniting machine HNM agriculture can be mainly estranged to 3 types: nutrient film technique (NFT),
Aquaponics and Deep Flow method. The NFT is 1 of the hydroponic ways such that a thin torrent of water having
the compulsory dissolved nutrients otherwise known as NS is used that is an upright standard for development of
the plant. This one is re-disseminated in the plants roots in the pipelines that are watertight, furthermore called

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6681-6690
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otherwise as channels. In this scheme, NS is augmented with substances such as rock wool, sand that is
conceded and re- disseminated inside a slope containing of plants positioned in a plastic furrow [3], [4].
This delivers the finest quantity of nutrients through its root system to the plant. HNM can also be called
as a capable apparatus in the zones having restricted land assets. It bids main benefits upon the age-old soil farming
and additional agriculture approaches. Low cost of installment, tranquil operation, supervision and actual
utilization of nutrients and recycling of water are few of the motives that has made it a prevalent option [5].
The area of HNM is comparatively new and coalescing it with progressing tools and calculation can
give cost and time effective resolutions. Few restrictions inside the zone of HNM contain the absence of:
i) active supervision of nutrients castoff in the hydroponic harvests water; ii) comprehensive investigation and
learning of factors which endorse quick development with decent quality harvests; and iii) relaxed and clever
ways, wherein mechanization procedures by means of ML can aid us in forecasting education methods with
HNM.
Diverse kinds of algorithms are castoff in order to project and forecast precise standards of the factors
convoluted throughout the development procedure of the yields. This will improve the effectiveness and make
HNM agriculture a relaxed and smart method to exploit yields. Hydroponic nutrient management is the practice
of supplying plants with the necessary nutrients they need to grow in a hydroponic system. Hydroponics is a
method of growing plants without soil, where plants are instead grown in a nutrient-rich water solution. Proper
nutrient management is crucial for ensuring optimal plant growth, health, and productivity in hydroponic systems.
2. RELATED WORK
The research work [6], [7] explores the submission of fuzzy algorithm Mamdani technique to forecast
the hydroponic watercress expansion. The idea in this effort is to utilize the fuzzy inference system (FIS) that
is fundamentally a calculating system which functions on the fuzzy reasoning principle and a human reasoning
similitude to forecast harvest development. Shan et al. [8] projected a scheme to assess the development of the
plant by means of internet of things (IoT) and a logic of fuzzy to preserve finest water supply and levels of
nutrients. The project is active when likened to the old-style approaches cast-off in HNM to assess the water
and nutrient necessities throughout the lifespan of the plant. When we see research [9], neural networks which
is artificial were pragmatic to a prototype which forecasts standards of electric conductivity (EC) and pH of
the scheme. Such forecasts remain highly significant as they are needed by supplementary intellectual schemes
which safeguard the correct and best process of the hydroponic system. This effort likewise attaches AI and
ML with hydroponic systems that unlocks the technique to additional progression and education of clever
systems in the arena of HNM which would lead to extra accurate farming [10].
3. PROPOSED HYDRO-CROP GROWTH PREDICTION SYSTEM
The planned system depicted in Figure 1, portrays the projected framework for forecast of complete
crop yield rate (CYR) by means of ML [11]. The stages trailed are data congregation that comprises water
sample investigation, which experiences the procedure of ion chromatography, to find ion concentrations. The
results coming through the data congregation and analytics phases would be served to the ML algorithm to
descend forecast consequences. The whole procedure is explained in three segments: design architecture,
forecast system, flowchart of the scheme [12].
Figure 1. Fundamental block diagram of HNM system overview

Int J Elec & Comp Eng ISSN: 2088-8708 
An efficient hydro-crop growth prediction system for nutrient analysis … (Chandana Chikkasiddaiah)
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3.1. System plan architecture
The whole arrangement is alienated into 3 chief portions: data fetching, analytics and forecast
consequences. The scheme is not rigid and can be utilized for yields appropriate for NFT. The harvests of
tomatos stayed cultured with a soilless NFT, a re-disseminated close scheme. Here, we deliberate samples of
tap water for examination though rain or recycled water can also be utilized in NFT. The temperature least
assessment was kept at 11.9 °
C, and the temperature of ventilation was fixed to 19.9 °
C. The nutrient solution
(NS) resolution could be accepted premixed depending on magnitude necessities [13], [14]. Each two days, all
cistern was replenished with renewed NS till the early volume. We also observe that the quantity of nutrients,
filths or salts inside the water, is raised to the limit of EC. In order to preserve finest levels of EC, researchers
fix the EC to the following points (5.1, 7.6 and 10.1 dS·m−1
).
EC-dependent nutrient regulator for HNM harvest farming might deliver quantities of nutrients which
are not in ideal quantities for the plants, hence cannot be cast-off unswervingly to find the ions concentration.
A procedure of ion chromatography Dionex model DX500; was utilized to show the complete liquefied ion
absorptions in the resolution [15], [16]. The info gave the standards for the ion concentration with in the several
portions of the tomatos that is sectioned into the fruits, roots leaf, stem. A least temperature of 12.1 °
C stood
fixed and temperature of ventilation was 20.1 °
C having a comparative humidity that remained continuously
upper than 50.1%, excluding for two days, where it touched 44.1%. Once in 15 days, lone plant from every trial
field was engaged for research. The density of the plant was witnessed to be 3.31 plants m−2
.
The parameters that were checked comprised complete plants dry matter weight along with EC limit,
NI ingestion, total CYR complete Nitrogen content of fruits and leaves, the ion absorptions for each organ
remained alienated as per their origin. Such particulars are utilized for the numerical data examination and the
highly correlated parameters shall be nourished into the ML algorithm to forecast a yield for the goal parameter.
Meanwhile the information is analyzed at every phase, it would assist in computing the elements concentration
hooked on to the NI and the plants composition of minerals on an everyday scenario [17], [18].
3.2. The dataset comprises
The whole dry matter load of the tomatos and dry weights of the dissimilar portions ingesting. NS
fresh plant, NI added plant and finally the NI remaining of the plant which is absent through the cycle and
towards the conclusion of it was measured, endorsement of inorganic cations, specifically magnesium (Mg),
sodium (Na), potassium (K), calcium (Ca) split for the different portions and is shown in gm L-1
d-1
.
3.3. Proposed prediction model
The prediction share of the scheme design is dependent on the ground of utilizing ML methods to
assess the complete plants CYR, ‘xx’ days forward in stint. The prototype targets to forecast the complete CYR
yield dependent over factors like: plants dry weight matter, NI, ion approvals of the fruit models prior and
subsequent yield, EC limit and whole nitrogen factor of the fruit. Overall CYR of the harvest could be computed
by the agreed formula [19].
𝑃𝑟𝑜𝑝𝑜𝑠𝑒𝑑 crop yield rate =
𝑋2− 𝑋1
𝜏2−𝜏1
(1)
where 𝑋1 and 𝑋2 is represents dry weights of the tomatos in grams with respect to the 𝜏1 and 𝜏2 days. Year
wise total crop yield prediction (𝐶𝑓)=∑ 𝑋𝑖
𝐴
− 𝑋𝑖
𝑃
12
𝑖=0 . Where 𝑋𝑖
𝐴
is represented the actual crop yield with respect
to month wise, 𝑋𝑖
𝑃
is described as the prediction crop yield with respect to month wise and i is identified as
number of months. Five-year total crop yield prediction (𝐶𝑓𝑓)=∑ 𝐶𝑓𝑗
𝐴
− 𝐶𝑓𝑗
𝑝
5
𝑗=0 . Where 𝐶𝑓𝑗
𝐴
is represented the
five wise actual crop yield, 𝐶𝑓𝑗
𝑝
is described the five wise prediction crop yield and j represented the number
years. Year wise total fertilizer utilization input parameters (𝐹𝑢)=∑ 𝐶𝑎𝑖
𝐴
+ 𝑀𝑔𝑖
𝐴
+ 𝐾𝑖
𝐴
+ 𝑁𝑎𝑖
𝐴
−
12
𝑖=0 𝐶𝑎𝑖
𝑃
−
𝑀𝑔𝑖
𝑃
− 𝐾𝑖
𝑃
− 𝑁𝑎𝑖
𝑃
. Where 𝐶𝑎𝑖
𝐴
is represented the year wise actual calcium ion, 𝑀𝑔𝑖
𝐴
is described as the year
wise actual magnesium ion, 𝐾𝑖
𝐴
is identified as actual potassium ion, 𝑁𝑎𝑖
𝐴
is described as the year wise actual
sodium ion, 𝐶𝑎𝑖
𝑃
is represented the year wise prediction calcium ion, 𝑀𝑔𝑖
𝑃
is described as the year wise
prediction magnesium ion, 𝐾𝑖
𝑃
is identified as prediction potassium ion and 𝑁𝑎𝑖
𝑃
is described as the year wise
prediction sodium ion. Five-year wise fertilizer utilization 𝐹𝑢𝑓 = ∑ 𝐹𝑢
𝐴
− 𝐹𝑢
𝑃
5
𝑗=0 . Where 𝐹𝑢
𝐴
is described the
five wise actual fertilizer utilization and 𝐹𝑢
𝑃
is identified as the five wise predicted fertilizer utilization.
3.4. System flowchart
The below phases define the method in more aspect and are revealed in Figure 2.
Phase 1: Gathering water trials for examination.
Phase 2: Setup and alteration system factors as per (tomatos) harvest needs EC limit range led lights range
410-710 nm and NI injection.

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Phase 3: In order to find ion concentration, ion chromatography is utilized.
Phase 4: Gathering for the analysis of nutrient data by enchanting input parameters such as ion concentration,
EC limit, NI, nitrogen content and complete plants dry weights.
Phase 5: Computation of the complete crop development ratio by means of (1).
Phase 6: Relationship amid input parameters and target parameter absolute CYR.
Phase 7: Submission of ML algorithm on the dataset of the parameters given.
Phase 8: Forecast consequences.
Figure 2. Proposed system of flowchart
4. DATA AND PREDICTION RATE ANALYSIS
4.1. Analysis of nutrients
A NI is considered as a liquid fertilizer resolution attuned in immovable arrangements so as to augment
harvest development and defend it through nutrient shortages. The plant’s requirement is satisfied over the
nutrients ionic form provided over the water and added nutrient fertilizer. The interpretations were engaged
over the duration of 140 days which is, 21 weeks around. The NI ingesting was mostly because of transpiration,
since in a NFT scheme the vaporization is negligible, mentioned for a lone plant [20], [21].
4.2. Analysis of fruit
The disparaging samples (prior yield) of tomatos plant were engaged each in the weeks of around 2
to 3 till 55 days after transplantation (DAT) to define the ion acceptance examination of fruits magnesium
(Mg), calcium (Ca), sodium (Na), potassium (K) and nitrogen (N) content. An obligatory draft oven was
utilized to compute the dry matter weight and an equilibrium with 2 places of decimal was utilized, all
observation standards are engaged in grams. The development factors comprised tomatos dry weight matter
and plants absolute CYR [22]–[26].

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5. RESULTS AND ANALYSIS
5.1. Complete development rate of the whole plant and stages of development of tomatos
The fruits were primarily seen on the plant through the early Phase I after 55 DAT, as depicted in
Figure 3. The tomatos complete CYR remained 2.65 gms/day through this phase. Subsequently 76 days after
NS explanation remained added numerous stints to the tomatos. In Phase II: (where in Fruit ripening happens)
next 92 days an exponential development rate of the plant was seen having a maximum total CYR of x.28
grams per day. The NS novel plant was added many periods through such phase as well. In phase III (where in
Fruit maturity and harvest happens), 117 DAT the fruits remained ripened and garnered, nevertheless a
reduction in the complete CYR was seen at this phase having a worth of 2.8 grams per day.
6
7
8
9
10
10
20
30
40
50
60
70
80
90
100
100
-4 -2 0 2 4
Absolute CYR
Days
after
Transplant
(DAT)
Absolute Crop yield Rate Analysis
Figure 3. The proposed NFT-HNM analysis of absolute CYR for every tomatos plant over the period of
20 weeks (140 days)
5.2. The tomatos growth rate in different stages of nutrient ion (before and after harvesting) analysis
and its impact on absolute CYR
The macro and micro-nutrient ion acceptances of the grape’s berries throughout the whole
development procedure of 140 days are shown in Tables 1 and 2. Grapes, like other plants, have specific macro
and micronutrient requirements for healthy growth and development. Here are some key macro and
micronutrients and their roles in grape cultivation.
Table 1. The tomatos crop yield rate (before harvesting)
Ion-Concentration (Before Harvesting in gms)
Crop yield Level NA Mg Ca N K DAT Proposed CYR
Phase_1 1.01 1.08 0.82 0 22.199 57 2.6
Phase_2
0.86 1.06 0.79 1.74 24.87 71 1.33
0.76 1.24 0.90 1.77 22.96 94 4.15
Phase_3
1.06 1.53 0.24 1.91 22.85 117 4.28
1.33 1.56 1.002 1.78 25.66 129 -0.66
1.66 1.75 1.009 1.80 24.54 142 -4.92
1.43 1.19 0.82 1.97 31.55 158 0.02
Table 2. The tomatos crop yield rate (after harvesting)
Ion-Concentration (After Harvesting in gms)
Crop yield Level NA Mg Ca N K DAT Dry Crop Weight
Phase_1 1.19 0.88 0.74 0 27.22 117 39.22
Phase_2
1.30 1.01 0.83 0 29.77 125 46.83
1.35 1.02 0.97 0 30.65 131 35.12
Phase_3
1.53 1.18 0.79 0 32.48 140 27.45
1.31 1.56 2.13 1.83 28.88 147 26.73
1.35 1.03 0.95 1.49 30.11 159 37.99
1.54 1.17 0.79 1.57 32.48 168 23.99

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Figures 4 and 5 shows the crop weight analysis of the tomatos before (I and phase II stages) and after
harvest (phase III stages) respectively using proposed system. Figures 6 and 7 shows the sodium ion analysis
of the tomatos before (I and phase II stages) and after harvest (phase III stages) respectively using proposed
system. The crop yield of tomatoes can vary depending on several factors, including cultivation practices,
environmental conditions, tomato variety, and management techniques.
Figure 4. Crop weight analysis of the tomatos
(before harvest) in phase I and phase II stages and
its analysis using proposed system
Figure 5. Crop weight analysis of the tomatos (after
harvest) in phase III stages and its analysis using
proposed system
Figure 6. Sodium ion analysis of the tomatos
Figure 7. Sodium ion analysis of the tomatos (after
proposed system
Figures 8 and 9 shows the magnesium ion analysis of the tomatos before (I and phase II stages) and
after harvest (phase III stages) respectively using proposed system. Figures 10 and 11 shows the calcium ion
analysis of the tomatos before (I and phase II stages) and after harvest (phase III stages) respectively using
proposed system. The crop yield of tomatoes can vary depending on several factors, including cultivation
practices, environmental conditions, tomato variety, and management techniques.
Figures 12 and 13 shows the nitrogen analysis of the tomatos before (I and phase II stages) and after
harvest (phase III stages) respectively using proposed system. Figures 14 and 15 shows the potassium analysis
of the tomatos before (I and phase II stages) and after harvest (phase III stages) respectively using proposed
system. An incessant distribution of NS was preserved until 168 days through the phase 3, that showed an
upsurge in the claim of ions by the plants, a minor upsurge of 0.099 in the CYR remained seen throughout the
later phases of yield.

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Figure 8. Magnesium ion analysis of the tomatos
Figure 9. Magnesium ion analysis of the tomatos
(after harvest) in phase III stages and its analysis
using proposed system
Figure 10. Calcium ion analysis of the tomatos
Figure 11. Calcium ion analysis of the tomatos
(after harvest) in phase III stages and its analysis
using proposed system
Figure 12. Nitrogen analysis of the tomatos (before
harvest) in phase I and phase II stages and its
analysis using proposed system
Figure 13. Nitrogen analysis of the tomatos (after
proposed system

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Figure 14. Potassium analysis of the tomatos
Figure 15. Potassium analysis of the tomatos (after
proposed system
The ion upsurge rates remained in the directive K>N>Na>Mg>Ca throughout the phases (earlier and
later yield). In the preliminary time of Phase I and Phase II (where in Fruit set and Fruit ripening happens) the
absorption of the ion upsurge was seen in the range (0 to 1.79) gm L-1
d-1
, nevertheless the K ion depicted an
important variant in the uptake with peak values of uptake in range (22 to 34) gm L-1
d-1
The acceptance rates
of the nutrients augmented expressively when the fruits matured and remained ready to yield till day 143. The
difference tendencies in ion absorption represent a growing tendency for all the ions after 94 and uppermost
absolute CYR (4.28 gm d-1
) was seen parallelly. In the future time a declining tendency was seen for the CYR
and captivation stages of all the ions excluding K ions correspondingly. The extreme uptake rates of K, N, Na,
Mg and Ca was 32.48, 1.91, 1.68, 1.75 and 1.25 gm L-1
d-1
correspondingly. Figure 15 depicts the nutrient
acceptances dynamics since day 117, the yield phases and its influence over the dry weight matter of the fruits.
The absorption of nitrogen remained seen to be too low and that is why it was not seen till day 138. Dry weight
substances of the fruits recorded an upsurge till 125. A quick reduction in weights was seen until day 147. The
tendencies in dissimilarity for Mg, N, Na, Ca and K ions did not track the tendencies in disparity of the dry
weight matter. Extreme concentrations standards seen were 1.68, 1.009, 1.75, 1.91,31.48 respectively.
We projected and premeditated a bendable structure for HNM which could be castoff in the forecast
of the complete CYR. The three phases in the scheme are dependent through each other and target to forecast
the CYR in a relaxed way using input parameters significant through the development procedure. The
ecological situations fixed in the NFT closed hydroponic scheme remained decent to assist the ideal farming
of the tomatos. A thorough investigation depicts the nutrient concentrations and undercurrents of ions uptakes
of Na Ca, N, Mg, K at diverse phases of tomatos development. The absolute CYR of the plant was computed
and perceived for every dissimilar phase of the. Any important variations were not seen amid the absolute CYR
and the dissimilar phases of fruit development. The association investigation later recommended the input
parameters that had a robust influence on the development factors similar to dry weight of the complete fruits.
It stood like nutrient ion Na required the uppermost association, such ions improve the progress and expansion
of the tomatos and likewise donate in refining the flavors of the eatable parts. Ca had a bottommost rate of
acceptance and the feeblest association in the dry weight matter of fruits, showing the quantity in the nutrient
elucidation seen was not ideal for development of tomatos. The K and Mg ions had optimistic relationships
and dissimilarity trends through the development procedure. Raised stages of K ions can progress the fruits.
Sophisticated EC values show additional ions in the result. The EC boundary persisted perpetual (5, 7.5, 10)
through the development of tomatos showing that a comparable quantity of ions was seen at this intermission.
Such results show that precise ion monitoring and adjustments are for healthier uptakes that can outcome in
respectable development and growth of the plants in HNM.
6. CONCLUSION
The proposed hydro-crop growth prediction system (HCGPS) is used to predict crop yield rate using
machine learning by adopting HNM method. The proposed system’s process is reconfigured to reduce the
complexity to achieve the better-quality crop growth and yield rate. The proposed HNM is used to analyze
Effects of straw return with N fertilizer reduction on crop yield, plant diseases and pests and potential heavy

6689
metal risk in a Chinese rice paddy: A field study of 2 consecutive wheat-rice cycles the water nutrients and its
concentration to get better crop growth and yield rate. The proposed method improves the Na and K ion uptake
0.98% and 1.025% respectively as compared to conventional methods. According to the analysis, the Ca ion
utilization is very low (0.78%) during entire crop growth process. this indicates better adjustment of NI. The
proposed HCGPS regularly monitors the various ions instead of ECL based control. due to this, it prevents the
nutrient deficiencies during absorption process to get better yield of tomatos. The machine learning based
HCGPS provides the information and prediction rate of water nutrients index and reduces the complexity
during the process to get better crop yield prediction rate.
7. FUTURE SCOPE
The proposed system can predict the crop yield rate using machine learning algorithm. But when
prediction analysis will be done on huge varieties of crops and climate changes, the system has to be developed
using random forest and deep neural network.
ACKNOWLEDGEMENTS
This work is partially supported by the REVA University, JSS Academy of Technical Education,
Bengaluru for all the support and VGST K-FIST L2 Sponsored Smart Grid Laboratory at JSS Academy of
Technical Education, Bengaluru encouragement provided by them to take up this research work and publish
this paper.
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BIOGRAPHIES OF AUTHORS
Chandana Chikkasiddaiah currently working as Assistant Professor in the
department of Master of Computer Applications, JSS Academy of Technical Education,
Bengaluru. She received his Bachelor’s Degree in Computer Science and Engineering from
Coorg Institute of Technology, South Kodagu, Karnataka, India during the year 2008 and M.
Tech in Computer Science and Engineering from SJCIT, Bengaluru, Karnataka, India during the
year 2014. She is having close to 14 years of teaching experience. She is available in this mail
id: punyachandu@gmail.com.
Parthasarathy Govindaswamy Associate Professor at School of Computing and
Information Technology, REVA University, Bangalore. He has pursued his Doctorate (Ph.D.)
degree in Computer Science and Engineering department at Sathyabma University, Chennai,
India in 2017. He received the M.E. degree from the Department of Computer Science and
Engineering, Sathyabama University, Chennai, India in 2006. He has vast experience of more
than 16 years in teaching under various colleges and Universities. He has published thirty papers
in international level Journals and Conferences. He has published 8 patents in various domains.
He is playing the role of reviewer for about four International reputed Journals like J. of
Information and Knowledge Management (JIKM) and Conferences. He is a Microsoft Certified
Professional and he is a member of Computer Society of India (CSI) and ACM. His research
interests are in data mining, web mining, big data analytics, cloud computing, sentiment analysis
and information retrieval. He is available in this mail id: parthasarathy.g@reva.edu.in.
Mallikarjunaswamy Srikantaswamy is currently working as an associate professor
in Department of Electronics and Communication Engineering at JSS Academy of Technical
Education, Bangalore. He obtained his B.E. degree in telecommunication engineering from
Visvesvaraya Technological University Belgaum in 2008, M.Tech. degree from Visvesvaraya
Technological University Belgaum in 2010 and was awarded Ph.D. from Jain University in 2015.
He has 11+ years of teaching experience. His research work has been published in more than 65
International Journals and conference. He received funds from different funding agencies.
Currently guiding five research scholars in Visvesvaraya Technological University Belgaum. He
can be contacted at email: mallikarjunaswamys@jssateb.ac.in.

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