Ranjan Baghel- B.E.(CSE),M.E.(CSE) PhD.(CSE)
- Researcher at Dr. Abdul Kalam Technical University, Lucknow
Ranjan Baghel
- B.E.(CSE),M.E.(CSE) PhD.(CSE)
- Researcher at Dr. Abdul Kalam Technical University, Lucknow
About
9
Publications
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Citations
Introduction
Ranjan Baghel currently works at the Ayushman Bharat project as District Information Systems Manager. Ranjan does research in Algorithms, Artificial Neural Network and Artificial Intelligence,. His most recent publications are 'Privacy preserving classification by using modified C4.5'
and Historical wheat yield mapping using time-series satellite data and district-wise yield statistics over Uttar Pradesh state, India
Current institution
Dr. Abdul Kalam Technical University, Lucknow
Current position
- Researcher
Additional affiliations
June 2009 - June 2012
May 2006 - June 2018
Publications
Publications (9)
Climate change and anthropogenic activities (changes in rainfall and temperature pattern) activities have significantly affected agricultural activity and crop production. So, studying cropland greenness and crop yield trends is essential to understand their impacts and to ensure food security. The present study attempted to depict the cropland gre...
Crop productivity has often been affected by undesirable climatic events such as heat stress, flood, unseasonal rainfall, drought, etc. Historical crop yield estimation and mapping shall provide a pivotal input for food security measures and planning purposes. The present study deployed various satellite (i.e., normalized difference vegetation inde...
Protecting the datasets supplied to third parties for data mining purposes is essential so that these datasets cannot be used for secondary purposes. C4.5 is a classification algorithm which works on mixed datasets. Data perturbation is an important technique in data privacy. This paper proposes a modified C4.5 which uses perturbed and unrealized d...
Decision trees are tree shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset.C4.5 is an important classification algorithm. Data security is essential for every data owner. Unrealization approach is based on dataset complementation approach and is an important privacy protecting appr...
Relational databases are based on the theory of relational algebra because all the operations of RDBMS draw their functioning from the operations in relational algebra. The operations of relational algebra are defined on the sets, however, In general, the datamining algorithms requires databases which adopts the multiset philosophy to give better a...
Wireless and mobile networks are evolving very rapidly. The mobile nodes in the wireless networks are having multiple interfaces with different radio access technologies (RATs) which are having different capabilities, cost and performance ratio. The use of non-PC based portable devices is increasing due to their flexible usage. The wireless and mob...
Questions
Questions (6)
i want to lebel different crop pixels in an ndvi image
for e in NDVI_crops:
NDVI_crops2 = e.data[~e.mask] # Exclude non-cropland pixel values
NDVI_crops2 = NDVI_crops2[~np.isnan(NDVI_crops2)] # Exclude poor quality pixel values
n = len(NDVI_crops2) # Count of array
min_val = float(format((np.min(NDVI_crops2)), '.4f')) # Minimum value in array
max_val = float(format((np.max(NDVI_crops2)), '.4f')) # Maximum value in array
range_val = (min_val, max_val) # Range of values in array
mean = float(format((np.mean(NDVI_crops2)), '.4f')) # Mean of values in array
std = float(format((np.std(NDVI_crops2)), '.4f')) # Standard deviation of values in array
var = float(format((np.var(NDVI_crops2)), '.4f')) # Variance of values in array
median = float(format((np.median(NDVI_crops2)), '.4f')) # Median of values in array
quartiles = np.percentile(NDVI_crops2, [25, 75]) # 1st (25) & 3rd (75) quartiles of values in array
upper_quartile = float(format((quartiles[1]), '.4f'))
lower_quartile = float(format((quartiles[0]), '.4f'))
iqr = quartiles[1] - quartiles[0] # Interquartile range
iqr_upper = upper_quartile + 1.5 * iqr # 1.5 IQR of the upper quartile
iqr_lower = lower_quartile - 1.5 * iqr # 1.5 IQR of the lower quartile
top = float(format(np.max(NDVI_crops2[NDVI_crops2 <= iqr_upper]), '.4f')) # Highest datum within 1.5 IQR of upper quartile
bottom = float(format(np.min(NDVI_crops2[NDVI_crops2>=iqr_lower]),'.4f')) # Lowest datum within 1.5 IQR of lower quartile
It is giving error no pyhdf module found. classfactory() method error
Mod13q1 and mcd12q1 based explaination will be a plus.
how to get satellite image of 3 hectare field.
