Papers by Mohammed Ashraf
Eswar Publications, TN, India, 2020
Remote sensing is a method of reading objects and data from remote platforms like ground-based se... more Remote sensing is a method of reading objects and data from remote platforms like ground-based sensors,
aircraft, or satellites is a probably necessary supply of knowledge for in-field crop management, providing each
necessary information. The aim is to use Satellite perceived imagery data to create a completely different vegetation index as a method of assessing cover variation and its resulting impact on crop grain yield. Treatments consisted
of 5 N rates and 4 hybrids, that were big beneath irrigation close to Shelton, NE on a Hord silt dirt in 1997 and
1998. mental imagery information with 0.5-m the spatial resolution for collecting data from the craft on many
dates throughout each season employing multiple ways, four-filters [blue, green, red, and near-infrared
reflectance] camera system. imagery data were fitted into a geographical data system (GIS) and so registered,
introduction to coefficient, and accustomed 3 vegetation index. Grain yield for every plot was firm at maturity.
Results showed that inexperienced normalized distinction vegetation index (NDVI) values derived from pictures non-inheritable throughout mid-grain filling were the foremost extremely correlative with grain yield, most correlations were 0.7 and 0.92 in 1997 and 1998, mostly. NDVI and grain yield variability among hybrids
improved the correlations each year, however, a lot of action will increase were determined in the year 1997 is 0.7
to 0.82 then in the year 1998 is 0.92 to 0.95.NDVI imagery will help in crop monitoring and when we combine the
NDVI with GIS we can build the crop management. Other Satellites can be used for getting the soil-related data, as soil plays an important role in any agricultural production, And in the end, we need the weather report data to build the crop monitoring system. By using Satellite data we can reduce lots of costs.
International Research Journal of Modernization in Engineering Technology and Science , 2021
We have seen a huge growth in technology in recent years. The use of smartphones is increased rap... more We have seen a huge growth in technology in recent years. The use of smartphones is increased rapidly no
matter where you go you find at least one smartphone in every family; social networking sites have seen
incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of
data is generated day by day around 70% of world data are generated by social media sites. Unlike any other
social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment
analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this
paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But
first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a
machine learning method in which the model of the training data is present only in theory until you made any
prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local
regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In
Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes
Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will
be selected for further work.
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Papers by Mohammed Ashraf
aircraft, or satellites is a probably necessary supply of knowledge for in-field crop management, providing each
necessary information. The aim is to use Satellite perceived imagery data to create a completely different vegetation index as a method of assessing cover variation and its resulting impact on crop grain yield. Treatments consisted
of 5 N rates and 4 hybrids, that were big beneath irrigation close to Shelton, NE on a Hord silt dirt in 1997 and
1998. mental imagery information with 0.5-m the spatial resolution for collecting data from the craft on many
dates throughout each season employing multiple ways, four-filters [blue, green, red, and near-infrared
reflectance] camera system. imagery data were fitted into a geographical data system (GIS) and so registered,
introduction to coefficient, and accustomed 3 vegetation index. Grain yield for every plot was firm at maturity.
Results showed that inexperienced normalized distinction vegetation index (NDVI) values derived from pictures non-inheritable throughout mid-grain filling were the foremost extremely correlative with grain yield, most correlations were 0.7 and 0.92 in 1997 and 1998, mostly. NDVI and grain yield variability among hybrids
improved the correlations each year, however, a lot of action will increase were determined in the year 1997 is 0.7
to 0.82 then in the year 1998 is 0.92 to 0.95.NDVI imagery will help in crop monitoring and when we combine the
NDVI with GIS we can build the crop management. Other Satellites can be used for getting the soil-related data, as soil plays an important role in any agricultural production, And in the end, we need the weather report data to build the crop monitoring system. By using Satellite data we can reduce lots of costs.
matter where you go you find at least one smartphone in every family; social networking sites have seen
incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of
data is generated day by day around 70% of world data are generated by social media sites. Unlike any other
social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment
analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this
paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But
first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a
machine learning method in which the model of the training data is present only in theory until you made any
prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local
regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In
Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes
Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will
be selected for further work.
aircraft, or satellites is a probably necessary supply of knowledge for in-field crop management, providing each
necessary information. The aim is to use Satellite perceived imagery data to create a completely different vegetation index as a method of assessing cover variation and its resulting impact on crop grain yield. Treatments consisted
of 5 N rates and 4 hybrids, that were big beneath irrigation close to Shelton, NE on a Hord silt dirt in 1997 and
1998. mental imagery information with 0.5-m the spatial resolution for collecting data from the craft on many
dates throughout each season employing multiple ways, four-filters [blue, green, red, and near-infrared
reflectance] camera system. imagery data were fitted into a geographical data system (GIS) and so registered,
introduction to coefficient, and accustomed 3 vegetation index. Grain yield for every plot was firm at maturity.
Results showed that inexperienced normalized distinction vegetation index (NDVI) values derived from pictures non-inheritable throughout mid-grain filling were the foremost extremely correlative with grain yield, most correlations were 0.7 and 0.92 in 1997 and 1998, mostly. NDVI and grain yield variability among hybrids
improved the correlations each year, however, a lot of action will increase were determined in the year 1997 is 0.7
to 0.82 then in the year 1998 is 0.92 to 0.95.NDVI imagery will help in crop monitoring and when we combine the
NDVI with GIS we can build the crop management. Other Satellites can be used for getting the soil-related data, as soil plays an important role in any agricultural production, And in the end, we need the weather report data to build the crop monitoring system. By using Satellite data we can reduce lots of costs.
matter where you go you find at least one smartphone in every family; social networking sites have seen
incredible numbers of users’ peoples are using social media widely nowadays. As a result, a huge amount of
data is generated day by day around 70% of world data are generated by social media sites. Unlike any other
social media, many peoples are using Twitter to share their thoughts, opinions on many topics. Sentiment
analysis of Twitter is important to understand the true meaning behind it and it is used in many services. In this
paperwork, we going the use the lazy learning or lazy learning algorithm for Twitter sentiment analysis. But
first, we going to use the natural language processing (NLP) techniques for preprocessing. Lazy learning is a
machine learning method in which the model of the training data is present only in theory until you made any
prediction using the model. Lazy learning main has three algorithms K-nearest neighbors (KNN), Local
regression, and Naive Bayes rules. Here we going to use the KNN classifier and Naive Bayes rules classifier. In
Naive Bayes, we use Gaussian Naive Bayes, Multinomial Naive Bayes Classifier, and Bernoulli Naive Bayes
Classifier and compare all the lazy learning methods for classification Accuracy. The best accuracy method will
be selected for further work.