The document describes a study that used fuzzy C-means clustering and an adaptive neuro-fuzzy inference system to classify land use and land cover in Visakhapatnam, India from satellite images. Satellite images from 2012, 2014, and 2017 were collected and preprocessed using a hybrid directional lifting technique to remove noise. The preprocessed images were segmented using fuzzy C-means clustering. Local binary pattern and gray-level co-occurrence matrix features were then extracted from the segmented images. These features were input into an adaptive neuro-fuzzy inference system to classify the land use and land cover into four classes: water body, vegetation, settlement, and barren land. The proposed system achieved higher classification accuracy compared to existing methods.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
CENTROG FEATURE TECHNIQUE FOR VEHICLE TYPE RECOGNITION AT DAY AND NIGHT TIMESijaia
This work proposes a feature-based technique to recognize vehicle types within day and night times. Support vector machine (SVM) classifier is applied on image histogram and CENsus Transformed
histogRam Oriented Gradient (CENTROG) features in order to classify vehicle types during the day and night. Thermal images were used for the night time experiments. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable
for night time image capturing and subsequent analysis. Since contour is useful in shape based categorisation and the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were
compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better recognition accuracies for both day and night times vehicle types recognition.
Ecological gradient analyses of plant associations in the thandiani forests o...Shujaul Mulk Khan
Abstract: In the summers of 2012 and 2013, vegetation of Thandiani in the Western Himalayas of Pakistan was surveyed and quantified. We took evidence from relationships between 252 species and 11 measured environmental factors as well as changes in the associations’ structure among 50 analysed stations with 1500 m2 plots. We analysed how the plant associations differ and develop under the influence of their respective ecological gradients. Preliminary results showed that the family Pinaceae was the most abundant family with a
family importance value (FIV) of 1892.4, followed by Rosaceae with FIV = 1478.2. Rosaceae, represented by 20 species, was the most dominant family, followed by Asteraceae and Ranunculaceae with 14 and 12 species each, respectively. Analyses via CANOCO software version 4.5 and GEO database demonstrated strong correlations among species distributions and environmental variables, i.e. elevation, topography, and edaphic factors. Our findings show an increase in species diversity and richness from lower elevation (1290 m at sea level (m asl) to higher elevation (2626 m asl). It is evident that aspect, elevation, and soil factors were the decisive variables affecting qualitative and quantitative attributes of vegetation in the study area. The P value ≤ 0.002 confirms a significant impact of abiotic factors that bring variation in vegetation. A 3D view of the study area was generated in ArcScene showing all the five plant associations. Graphs of scatter plot, point profile, and 3D line profile were added to the layout of plant association maps. The habitats of the five association types overlapped broadly but still retained their specific individuality. The execution of GIS framework gave spatial modelling, which ultimately helped in the recognition of indicator species of specific habitat or association type. These findings could further be utilised
in devising the forest policy and conservation management. This study also opens new doors of research in the field of biogeography, systematics, and wildlife.
Intelligent Chemical Fertilizer Recommendation System for Rice Fields IIJSRJournal
In this paper, a recommendation system for supplementary chemical fertilizers of rice fields is proposed using data mining methods. Traditionally, an expert determines the necessary amount of chemical fertilizer for each field after testing the amount of existing organic materials in the soil. The recommendation provided by the expert is a combination of agricultural science and region-specific conditions. In this paper, thru recognizing the existing pattern in recommendations proposed by two groups of experts for the agricultural lands in Mazandaran Province in recent years, a predictive model is proposed. Different artificial intelligence techniques are compared with each other and the best one among them is introduced
Automatic registration, integration and enhancement of india's chandrayaan 1 ...eSAT Journals
Abstract Chandrayaan-1 was India's first mission in deep space exploration to the moon. Its Terrain Mapping Camera (TMC) sent images of about 50% of total lunar surface in its limited lifetime and covered polar areas almost completely at a high resolution of 5m/pixel and 10m/pixel. This image dataset has been processed and put in public domain as individual strips of images categorized according to the orbits. The authors have already developed a Lunar GIS including a set of utilities like 3-D vision and exploration, crater detection and search using datasets from NASA's Lunar Reconnaissance Orbiter Wide Angle Camera (WAC) which are of lower resolution than CH1. The objective of this paper is to normalize and register the Chandrayaan-1 images to existing processed data so that all these utilities can be transparently applied to high resolution Chandrayaan-1 datasets. Registration process consists of identification of features in source and target images and estimating appropriate correction for offset, rotation and scaling parameters. Furthermore, due to the low altitude orbit of satellite, the acquired images have displacement of pixels from actual nadir position, which need non-linear correction. This paper describes step by step technique to integrate these high and low resolution images in single framework. Keywords: Chandrayaan-1Lunar mapping, Moon, Feature based Image registration, Integration, ISRO, LRO, NASA, TMC, WAC.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
CENTROG FEATURE TECHNIQUE FOR VEHICLE TYPE RECOGNITION AT DAY AND NIGHT TIMESijaia
This work proposes a feature-based technique to recognize vehicle types within day and night times. Support vector machine (SVM) classifier is applied on image histogram and CENsus Transformed
histogRam Oriented Gradient (CENTROG) features in order to classify vehicle types during the day and night. Thermal images were used for the night time experiments. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable
for night time image capturing and subsequent analysis. Since contour is useful in shape based categorisation and the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were
compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better recognition accuracies for both day and night times vehicle types recognition.
Ecological gradient analyses of plant associations in the thandiani forests o...Shujaul Mulk Khan
Abstract: In the summers of 2012 and 2013, vegetation of Thandiani in the Western Himalayas of Pakistan was surveyed and quantified. We took evidence from relationships between 252 species and 11 measured environmental factors as well as changes in the associations’ structure among 50 analysed stations with 1500 m2 plots. We analysed how the plant associations differ and develop under the influence of their respective ecological gradients. Preliminary results showed that the family Pinaceae was the most abundant family with a
family importance value (FIV) of 1892.4, followed by Rosaceae with FIV = 1478.2. Rosaceae, represented by 20 species, was the most dominant family, followed by Asteraceae and Ranunculaceae with 14 and 12 species each, respectively. Analyses via CANOCO software version 4.5 and GEO database demonstrated strong correlations among species distributions and environmental variables, i.e. elevation, topography, and edaphic factors. Our findings show an increase in species diversity and richness from lower elevation (1290 m at sea level (m asl) to higher elevation (2626 m asl). It is evident that aspect, elevation, and soil factors were the decisive variables affecting qualitative and quantitative attributes of vegetation in the study area. The P value ≤ 0.002 confirms a significant impact of abiotic factors that bring variation in vegetation. A 3D view of the study area was generated in ArcScene showing all the five plant associations. Graphs of scatter plot, point profile, and 3D line profile were added to the layout of plant association maps. The habitats of the five association types overlapped broadly but still retained their specific individuality. The execution of GIS framework gave spatial modelling, which ultimately helped in the recognition of indicator species of specific habitat or association type. These findings could further be utilised
in devising the forest policy and conservation management. This study also opens new doors of research in the field of biogeography, systematics, and wildlife.
Intelligent Chemical Fertilizer Recommendation System for Rice Fields IIJSRJournal
In this paper, a recommendation system for supplementary chemical fertilizers of rice fields is proposed using data mining methods. Traditionally, an expert determines the necessary amount of chemical fertilizer for each field after testing the amount of existing organic materials in the soil. The recommendation provided by the expert is a combination of agricultural science and region-specific conditions. In this paper, thru recognizing the existing pattern in recommendations proposed by two groups of experts for the agricultural lands in Mazandaran Province in recent years, a predictive model is proposed. Different artificial intelligence techniques are compared with each other and the best one among them is introduced
Automatic registration, integration and enhancement of india's chandrayaan 1 ...eSAT Journals
Abstract Chandrayaan-1 was India's first mission in deep space exploration to the moon. Its Terrain Mapping Camera (TMC) sent images of about 50% of total lunar surface in its limited lifetime and covered polar areas almost completely at a high resolution of 5m/pixel and 10m/pixel. This image dataset has been processed and put in public domain as individual strips of images categorized according to the orbits. The authors have already developed a Lunar GIS including a set of utilities like 3-D vision and exploration, crater detection and search using datasets from NASA's Lunar Reconnaissance Orbiter Wide Angle Camera (WAC) which are of lower resolution than CH1. The objective of this paper is to normalize and register the Chandrayaan-1 images to existing processed data so that all these utilities can be transparently applied to high resolution Chandrayaan-1 datasets. Registration process consists of identification of features in source and target images and estimating appropriate correction for offset, rotation and scaling parameters. Furthermore, due to the low altitude orbit of satellite, the acquired images have displacement of pixels from actual nadir position, which need non-linear correction. This paper describes step by step technique to integrate these high and low resolution images in single framework. Keywords: Chandrayaan-1Lunar mapping, Moon, Feature based Image registration, Integration, ISRO, LRO, NASA, TMC, WAC.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
The effects of multiple layers feed-forward neural network transfer function ...IJECEIAES
In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Predicting the Spread of Acacia Nilotica Using Maximum Entropy ModelingTELKOMNIKA JOURNAL
Acacia nilotica planted in Baluran National Park aims to prevent the spread of fire from savanna
to teak forest became developed into invasive and led to a decrease in the quality and quantity of
savannas. Therefore, it is required to predict the spread of A. nilotica to minimize the impacts of invasion
on savanna area. The study aims to identify environmental factors which affect spread of A. nilotica.
Furthermore, the spread of A. nilotica is predicted using Maximum Entropy. Maximum Entropy is efficient
model since it uses presence-only data while the most of other models use presence and absence data.
The experimental results reveal six environmental factors, including elevation, slope, NDMI, NDVI,
distance from the river, and temperature were identified affecting the spread of A. nilotica. The most
dominant environmental factors were elevation and temperature with 40% and 39.6% contributions.
Maximum Entropy performed well in predicting the spread of A. nilotica, it was indicated by AUC value of
0.938.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have anaccurate model for rainfall prediction. Recently, several data-driven modeling approaches havebeen investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third of the data was used for training the model and One-third for testing.
Application of informative textural Law’s masks methods for processing space...IJECEIAES
Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.
Wearable sensor-based human activity recognition with ensemble learning: a co...IJECEIAES
The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.
Hyperparameters analysis of long short-term memory architecture for crop cla...IJECEIAES
Deep learning (DL) has seen a massive rise in popularity for remote sensing (RS) based applications over the past few years. However, the performance of DL algorithms is dependent on the optimization of various hyperparameters since the hyperparameters have a huge impact on the performance of deep neural networks. The impact of hyperparameters on the accuracy and reliability of DL models is a significant area for investigation. In this study, the grid Search algorithm is used for hyperparameters optimization of long short-term memory (LSTM) network for the RS-based classification. The hyperparameters considered for this study are, optimizer, activation function, batch size, and the number of LSTM layers. In this study, over 1,000 hyperparameter sets are evaluated and the result of all the sets are analyzed to see the effects of various combinations of hyperparameters as well the individual parameter effect on the performance of the LSTM model. The performance of the LSTM model is evaluated using the performance metric of minimum loss and average loss and it was found that classification can be highly affected by the choice of optimizer; however, other parameters such as the number of LSTM layers have less influence.
Fuzzy C-means clustering on rainfall flow optimization technique for medical ...IAESIJAI
Due to various killing diseases in the world, medical data clustering is a very
challenging and critical task to handle and to take the proper decision from
multidimensional complex data in an effective manner. The most familiar
and suitable speedy clustering algorithm is K-means than other traditional
clustering approaches. But K-means is extra sensitive for initialization of
clustering centroid and it can easily surround. Thus, there is a necessity for
faster clustering with an effective optimum clustering centroid. Based on
that, this research paper projected an optimization-based clustering by hybrid
fuzzy C-means (FCM) clustering on rainfall flow optimization technique
(RFFO), which is the normal flow and behavior of rainfall flow from one
position to another position. FCM clustering algorithm is used to cluster the
given medical data and RFFO is used to produce optimum clustering
centroid. Finally, the clustering performance is also measured for the
proposed FCM clustering on RFFO technique with the help of accuracy,
random coefficient, and Jaccard coefficient for medical data set and find the
risk factor of a heart attack.
Health monitoring catalogue based on human activity classification using mac...IJECEIAES
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
IMPROVED NEURAL NETWORK PREDICTION PERFORMANCES OF ELECTRICITY DEMAND: MODIFY...csandit
Accurate prediction of electricity demand can bring extensive benefits to any country as the
forecast values help the relevant authorities to take decisions regarding electricity generation,
transmission and distribution much appropriately. The literature reveals that, when compared
to conventional time series techniques, the improved artificial intelligent approaches provide
better prediction accuracies. However, the accuracy of predictions using intelligent approaches
like neural networks are strongly influenced by the correct selection of inputs and the number of
neuro-forecasters used for prediction. This research shows how a cluster analysis performed to
group similar day types, could contribute towards selecting a better set of neuro-forecasters in
neural networks. Daily total electricity demands for five years were considered for the analysis
and each date was assigned to one of the thirteen day-types, in a Sri Lankan context. As a
stochastic trend could be seen over the years, prior to performing the k-means clustering, the
trend was removed by taking the first difference of the series. Three different clusters were
found using Silhouette plots, and thus three neuro-forecasters were used for predictions. This
paper illustrates the proposed modified neural network procedure using electricity demand
data.
Feature Selection Approach based on Firefly Algorithm and Chi-square IJECEIAES
Dimensionality problem is a well-known challenging issue for most classifiers in which datasets have unbalanced number of samples and features. Features may contain unreliable data which may lead the classification process to produce undesirable results. Feature selection approach is considered a solution for this kind of problems. In this paperan enhanced firefly algorithm is proposed to serve as a feature selection solution for reducing dimensionality and picking the most informative features to be used in classification. The main purpose of the proposedmodel is to improve the classification accuracy through using the selected features produced from the model, thus classification errors will decrease. Modeling firefly in this research appears through simulating firefly position by cell chi-square value which is changed after every move, and simulating firefly intensity by calculating a set of different fitness functionsas a weight for each feature. Knearest neighbor and Discriminant analysis are used as classifiers to test the proposed firefly algorithm in selecting features. Experimental results showed that the proposed enhanced algorithmbased on firefly algorithm with chisquare and different fitness functions can provide better results than others. Results showed that reduction of dataset is useful for gaining higher accuracy in classification.
The effects of multiple layers feed-forward neural network transfer function ...IJECEIAES
In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Predicting the Spread of Acacia Nilotica Using Maximum Entropy ModelingTELKOMNIKA JOURNAL
Acacia nilotica planted in Baluran National Park aims to prevent the spread of fire from savanna
to teak forest became developed into invasive and led to a decrease in the quality and quantity of
savannas. Therefore, it is required to predict the spread of A. nilotica to minimize the impacts of invasion
on savanna area. The study aims to identify environmental factors which affect spread of A. nilotica.
Furthermore, the spread of A. nilotica is predicted using Maximum Entropy. Maximum Entropy is efficient
model since it uses presence-only data while the most of other models use presence and absence data.
The experimental results reveal six environmental factors, including elevation, slope, NDMI, NDVI,
distance from the river, and temperature were identified affecting the spread of A. nilotica. The most
dominant environmental factors were elevation and temperature with 40% and 39.6% contributions.
Maximum Entropy performed well in predicting the spread of A. nilotica, it was indicated by AUC value of
0.938.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have anaccurate model for rainfall prediction. Recently, several data-driven modeling approaches havebeen investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third of the data was used for training the model and One-third for testing.
Application of informative textural Law’s masks methods for processing space...IJECEIAES
Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.
Wearable sensor-based human activity recognition with ensemble learning: a co...IJECEIAES
The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.
Hyperparameters analysis of long short-term memory architecture for crop cla...IJECEIAES
Deep learning (DL) has seen a massive rise in popularity for remote sensing (RS) based applications over the past few years. However, the performance of DL algorithms is dependent on the optimization of various hyperparameters since the hyperparameters have a huge impact on the performance of deep neural networks. The impact of hyperparameters on the accuracy and reliability of DL models is a significant area for investigation. In this study, the grid Search algorithm is used for hyperparameters optimization of long short-term memory (LSTM) network for the RS-based classification. The hyperparameters considered for this study are, optimizer, activation function, batch size, and the number of LSTM layers. In this study, over 1,000 hyperparameter sets are evaluated and the result of all the sets are analyzed to see the effects of various combinations of hyperparameters as well the individual parameter effect on the performance of the LSTM model. The performance of the LSTM model is evaluated using the performance metric of minimum loss and average loss and it was found that classification can be highly affected by the choice of optimizer; however, other parameters such as the number of LSTM layers have less influence.
Fuzzy C-means clustering on rainfall flow optimization technique for medical ...IAESIJAI
Due to various killing diseases in the world, medical data clustering is a very
challenging and critical task to handle and to take the proper decision from
multidimensional complex data in an effective manner. The most familiar
and suitable speedy clustering algorithm is K-means than other traditional
clustering approaches. But K-means is extra sensitive for initialization of
clustering centroid and it can easily surround. Thus, there is a necessity for
faster clustering with an effective optimum clustering centroid. Based on
that, this research paper projected an optimization-based clustering by hybrid
fuzzy C-means (FCM) clustering on rainfall flow optimization technique
(RFFO), which is the normal flow and behavior of rainfall flow from one
position to another position. FCM clustering algorithm is used to cluster the
given medical data and RFFO is used to produce optimum clustering
centroid. Finally, the clustering performance is also measured for the
proposed FCM clustering on RFFO technique with the help of accuracy,
random coefficient, and Jaccard coefficient for medical data set and find the
risk factor of a heart attack.
Health monitoring catalogue based on human activity classification using mac...IJECEIAES
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
IMPROVED NEURAL NETWORK PREDICTION PERFORMANCES OF ELECTRICITY DEMAND: MODIFY...csandit
Accurate prediction of electricity demand can bring extensive benefits to any country as the
forecast values help the relevant authorities to take decisions regarding electricity generation,
transmission and distribution much appropriately. The literature reveals that, when compared
to conventional time series techniques, the improved artificial intelligent approaches provide
better prediction accuracies. However, the accuracy of predictions using intelligent approaches
like neural networks are strongly influenced by the correct selection of inputs and the number of
neuro-forecasters used for prediction. This research shows how a cluster analysis performed to
group similar day types, could contribute towards selecting a better set of neuro-forecasters in
neural networks. Daily total electricity demands for five years were considered for the analysis
and each date was assigned to one of the thirteen day-types, in a Sri Lankan context. As a
stochastic trend could be seen over the years, prior to performing the k-means clustering, the
trend was removed by taking the first difference of the series. Three different clusters were
found using Silhouette plots, and thus three neuro-forecasters were used for predictions. This
paper illustrates the proposed modified neural network procedure using electricity demand
data.
Feature Selection Approach based on Firefly Algorithm and Chi-square IJECEIAES
Dimensionality problem is a well-known challenging issue for most classifiers in which datasets have unbalanced number of samples and features. Features may contain unreliable data which may lead the classification process to produce undesirable results. Feature selection approach is considered a solution for this kind of problems. In this paperan enhanced firefly algorithm is proposed to serve as a feature selection solution for reducing dimensionality and picking the most informative features to be used in classification. The main purpose of the proposedmodel is to improve the classification accuracy through using the selected features produced from the model, thus classification errors will decrease. Modeling firefly in this research appears through simulating firefly position by cell chi-square value which is changed after every move, and simulating firefly intensity by calculating a set of different fitness functionsas a weight for each feature. Knearest neighbor and Discriminant analysis are used as classifiers to test the proposed firefly algorithm in selecting features. Experimental results showed that the proposed enhanced algorithmbased on firefly algorithm with chisquare and different fitness functions can provide better results than others. Results showed that reduction of dataset is useful for gaining higher accuracy in classification.
Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble
Outdoor Air Quality Monitoring with Enhanced Lifetime-enhancing Cooperative Data Gathering and Relaying Algorithm (E-LCDGRA) Based Sensor Network
Data Analytics of an Information System Based on a Markov Decision Process and a Partially Observable Markov Decision Process
On Software Application Database Constraint-driven Design and Development
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Analytical framework for optimized feature extraction for upgrading occupancy...IJECEIAES
The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.
Novel Methodology of Data Management in Ad Hoc Network Formulated using Nanos...Drjabez
In Ad hoc Network of Nanosensors for Wastage detection, clustering assist in nodal communication and in organization of the data fetched by the nanosensors in the network. The attempt of traditional cluster formation techniques degraded the formation of cluster in a precise manner. The data from the nanosensors which act as the nodes of the network have to be distinctively added into the clusters. The dynamic path selection cluster would achieve this distinct addition by dynamically creating a path to the data as an initial process and then redirecting the data to their appropriate cluster based to the readied scheme.
Solar Irradiation Prediction using back Propagation and Artificial Neural Net...ijtsrd
Solar Energy is one of most promising potential renewable sources of energy. But among all the conventional sources of renewable energy, its nature is quite unpredictable owing to the fact that the solar irradiation keeps on changing and fluctuating. Additional analysis mechanisms such as training states has been also presented which depicts how the mean square error plummets as the number of iterations increase. The variation of mean square error can be seen in training, testing and validation phases. The neural network topology used is 1 20 1 indicating one neuron in the input layer, 20 neurons in the hidden layer and 1 neuron in the output layer respectively. It has been shown that the proposed methodology attains a very good accuracy of approximately 97.74 with the error rate amounting to a meager 2.76 . This model serves to be a robust mechanism and shows good performance. The low error and high accuracy can be attributed to the efficacy of back propagation in Artificial Neural Networks. A comparative analysis is also presented with contemporary work that attains an error of 30 , proving the fact that the proposed system outperforms the contemporary techniques. Harendra Kumar Verma | Ashish Bhargava "Solar Irradiation Prediction using back Propagation and Artificial Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43673.pdf Paper URL: https://www.ijtsrd.comengineering/electrical-engineering/43673/solar-irradiation-prediction-using-back-propagation-and-artificial-neural-network/harendra-kumar-verma
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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2. Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
Adaptive Neuro-Fuzzy Inference System
http://www.iaeme.com/IJCIET/index.asp 383 editor@iaeme.com
Cite this Article: Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V.
Madhusudhana Rao, Land Use and Land Cover Classification For Visakhapatnam
Using Fuzzy C Means Clustering and Adaptive Neuro-Fuzzy Inference System,
International Journal of Civil Engineering and Technology (IJCIET), 10 (1), 2019, pp.
382–395.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1
1. INTRODUCTION
In present scenario, LU and LC classification using remote sensing image plays an essential
role in numerous applications like biological resources (fragmentation, wetlands, and habitat
quality), agricultural practice (riparian zone buffers, conservation easements, cropping
patterns, and nutrient management), land use planning (suburban sprawl, growth trends,
policy regulations and incentives) [1-2], and forest management (resource-inventory,
harvesting, health, stand-quality, and reforestation) [3-4]. Generally, the remote sensing
images delivers large scale and up-to date information about the earth surface condition. The
present remote sensing image has two major issues; maintaining the large volume of data and
noise associated with the image [5-6]. To address these issues, numerous methodologies are
developed by the researchers such as, artificial neural network [7], support vector machine
[8], hybrid classification [9], extreme gradient boosting classifier [10], etc. The conventional
methods in LU and LC classification are extremely affected by the environmental changes
like haphazard, uncontrolled urban development, destruction of essential wetlands, loss of
prime agricultural lands, deteriorating environmental quality, etc.
To address these concerns and also to enhance the LU and LC classification, a new
supervised system was developed in this research. Here, the satellite images were collected
for Visakhapatnam city in three different time periods: 2012, 2014, and 2017. The unwanted
noises, saturation and blooming effects in the collected satellite images were eliminated by
using HDL pre-processing technique. Additionally, the HDL technique retains the essential
details and also to improve the visual appearance of the images. The respective pre-processed
satellite images were used for segmentation by employing FCM clustering. The major
advantage of FCM clustering was very robust to clustering parameters that help to decrease
the computational charges. Then, hybrid feature extraction was carried-out to extract the
features from the segmented images. The hybrid feature extraction comprises of LBP and
GLCM (Homogeneity and energy)) features, which were utilized to obtain the feature subsets
from the set of data inputs by the rejection of redundant and irrelevant features. These feature
values were given as the input for ANFIS classifier to classify the LU and LC classes; water-
body, vegetation, settlement, and barren land.
This research paper is structured as follows. Section 2 denotes a broad survey of recent
papers in LU and LC classification. In section 3, an effective supervised system is developed
for LU and LC classification. In section 4, comparative and quantitative evaluation of
proposed and existing systems are presented. The conclusion is done in the 5.
2. LITERATURE REVIEW
Several new systems are developed by the researchers in LU and LC classification. In this
section, the evaluation of a few essential contributions to the existing literature papers are
presented.
Usually, automatic LU and LC classification helps the policy makers to understand the
environmental changes for ensuring the sustainable development. Hence, LU and LC feature
identification and classification have emerged as an essential research area in the field of
remote sensing. S. Sinha, L.K. Sharma, and M.S. Nathawat, [11] utilized maximum likelihood
3. Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V. Madhusudhana Rao
http://www.iaeme.com/IJCIET/index.asp 384 editor@iaeme.com
classifier for LU and LC classification. This research improved the classification accuracy
with the combined use spectral and thermal information from the satellite imageries. The
developed classification approach was only suitable for a minimum number of classes not for
maximum number of classes.
B. Gong, J. Im, and G. Mountrakis, [12] developed an effective optimized classification
algorithm: artificial immune network for LU and LC classification. The developed algorithm
helps in preserving the best anti-bodies of every LU and LC classes from ant-body population
suppression and also the mutation rates were self-adaptive on the basis of model performance
between training generations. In this research study, the spectral angle mapping distance and
Euclidean distance were used for measuring the affinity between the feature vectors. Finally,
genetic algorithm-based optimization was applied for better discriminate between the LU and
LC classes with similar properties. A major concern in the developed optimized classification
algorithm was high computational time, which was quite high compared to the other systems
in LU and LC classification.
A.K. Thakkar, V.R. Desai, A. Patel, and M.B. Potdar, [13] presented a new system for LU
and LC classification. The main goal of this research study was to extract the best LU and LC
information for Gujarat region (India) in dissimilar time periods: 2001, and 2011. In this
research study, the maximum likelihood classifier was used to IRS LISS-III imagery of 2011
and 2001 for classifying the LU and LC classes: agricultural land, prosopis or scrub forest,
built-up area, water-body, forest, barren land, river sand, and quarry. At last, a new
framework (normalized difference water index and drainage network) was applied for post
classification corrections. Here, a pre-processing method was required for further enhancing
the LU and LC classification.
H. Zhang, J. Li, T. Wang, H. Lin, Z. Zheng, Y. Li, and Y. Lu, [14] developed a new
approach for combining the synthetic aperture and optical radar data (radar SPOT-5 data) for
improving the LU and LC classes. In this literature paper, principle component analysis, local
linear embedding and ISOMAP were employed with three dissimilar synthetic and optical
apertures. In the experimental phase, the developed approach performance was evaluated by
means of classification accuracy and kappa co-efficient. In a large sized satellite image
dataset, the developed approach failed to accomplish better LU and LC classification.
Q. Chen, G. Kuang, J. Li, L. Sui, and D. Li, [15] presented a new un-supervised system
for LU and LC classification on the basis of polarimetric scattering similarity. The developed
system includes minor and major scattering mechanisms, which were identified automatically
based on the multiple scattering similarity magnitudes. Additionally, the canonical scattering
corresponds to the maximum scattering similarity, which was observed as the main scattering
mechanism. The obtained result using jet propulsion laboratory’s AIRSAR L-band PolSAR,
national aeronautics, space administration imagery exposes that the developed approach was
more effective related to other existing systems. In this literature study, the developed un-
supervised system did not focus on the segmentation that was considered as one of the major
concerns.
To overcome the above mentioned problems, an effective supervised system was
developed for improving the performance of LU and LC classification.
3. PROPOSED SYSTEM
Urbanization growth is a process of changing rural life-style into urban ones, which is
characterized as the progressions that happen in the territorial and socio-economic progress of
a zone, including the general changes of LU and LC classification from being non-developed
to develop. Here, it is essential to analyze and study the drastic changes happened due to
global urbanization periodically. In this research study, a new system was proposed to analyze
4. Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
Adaptive Neuro-Fuzzy Inference System
http://www.iaeme.com/IJCIET/index.asp 385 editor@iaeme.com
the urbanization changes occurred in Visakhapatnam city. The proposed system comprises of
five phases: image collection, pre-processing of collected image, segmentation, feature
extraction and classification. The work flow of proposed system is denoted in the Fig. 1.
Figure 1 Work flow of proposed system
3.1. Image collection
The satellite image utilized for LU and LC classification was collected from the 1.5km spatial
resolution of SEA Wi-FS data. Here, Visakhapatnam city is considered as a study area, which
is located at 17.686815 of latitude and 83.218483 of longitude and nearer to the Coromandel
Coast of the Bay of Bengal. The satellite image of Visakhapatnam city is collected for three
years; 2012, 2014, and 2017. The sample collected satellite image is denoted in the Fig. 2.
Figure 2 Sample image of Visakhapatnam city (year; 2017)
3.2. Pre-processing using HDL approach
The HDL approach varies from the traditional pre-processing approaches in orientation
evaluation and pixel classification. In satellite image denoising, the HDL approach comprises
of three important phases; pixel classification, orientation estimation and hybrid transform.
The image pixel classification results into the pixels belonging to two groups namely; smooth
and texture regions. Here, the orientation evaluation is performed on the basis of pixel
5. Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V. Madhusudhana Rao
http://www.iaeme.com/IJCIET/index.asp 386 editor@iaeme.com
correlation and classification. Finally, hybrid transform accomplishes the transform on pixel
level instead of block-based transform for avoiding artifacts in the smooth regions.
3.2.1. Image pixel classification
The input satellite image has two regions; texture and smooth region. In this technique, the
smooth and texture regions are set by using the flag and threshold values. A flag value (zero)
represents a smooth region and (one) represents a texture region. Hence, a flag value indicates
the local activity of every pixel in the satellite image. In this pre-processing technique, two
classification phases are performed in order to accomplish pixel classification. At first, the
satellite image is sub-categorized into sub-blocks and these sub-blocks are further
classified into Region of Non-Interest (RONI) and region of interest (ROI). Secondly, the
pixel classification process is performed on every pixel of ROI instead of sub-blocks. The
collected satellite image is sub-classified into smooth and texture regions by using the Eq. (1)
and (2).
( )
( ) ( ) (1)
( )
( ) ( ) (2)
Where, ( ) is represented as the satellite image pixels, is denoted as the threshold
value, which ranges from 0.1 to 0.6, ( )is specified as the local window variance of the
image pixels, is denoted as the noisy image variance. The ( ) is utilized for
separating the noisy image into smooth and texture regions on the basis of threshold .
3.2.2. Direction evaluation
The precision of direction evaluation is the key factor for obtaining good denoising
performance. Initially, the gradient factors and are considered and then the convolution
of a satellite image along with the gradient factors are calculated for estimating the
orientation, which is mathematically represented in the Eq. (3) and (4).
∑ ∑ ( ) , where , - (3)
∑ ∑ ( ) , where , - (4)
Where, and are denoted as the size of a satellite image and and
are represented as the new convolution matrices. At last, the direction information of
image pixel is evaluated by using the Eq. (5).
( ) ( ) (5)
3.2.3. Direction modification
In the ROI blocks, image pixels are further sub-divided into two types; pixels belong to the
smooth regions and pixels belongs to the image edges in order to modify the direction of
every pixel that is mathematically given in the Eq. (6). Though, it is very hard to calculate the
directional transform of pixels in the smooth regions.
( ) ( ) ( ) (6)
6. Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
Adaptive Neuro-Fuzzy Inference System
http://www.iaeme.com/IJCIET/index.asp 387 editor@iaeme.com
3.2.4. Hybrid transform
In this sub-section, the HDL technique utilizes a pixel based classified image ( ), which is
the resultant image of Bayesian classification method. Then, the minimum direction
estimation is obtained by utilizing ( ) and directional information of the satellite image
( ). The main aim of hybrid transform is to diminish the noise occurred in the
smooth image. The minimum direction estimation is calculated by using the Eq. (7) and (8).
( ) ( ) , ( ) ( )- (7)
( ) ( ) (8)
Then, the estimated minimum direction ( ) is added to the smooth region
of the satellite image in order to obtain the hybrid transform ( ), which is represented in
the Eq. (9).
( ) ( ) ( ) (9)
The computed hybrid value is subtracted from the small random value matrix ( ) that
ranges from 0 to 3. Finally, the denoised satellite image ( ) is obtained by using the Eq.
(10).
( ) ( ) ( ) (10)
Where, ( ) is represented as the small random number matrix. Fig. 3 represents the
pre-processed image after applying HDL technique.
Figure 3 Sample pre-processed image after applying HDL technique (year; 2017)
3.3. Segmentation using FCM algorithm
After pre-processing the input satellite image, FCM algorithm is used for segmenting the LU
and LC classes from a satellite image. In existing segmentation algorithms, it is hard to
segment the ill-defined portions that greatly decreases the segmentation accuracy. To address
this concern, FCM algorithm is used in this research for localizing the object in complex
template. Generally, FCM adopts fuzzy set theory for assigning a data object to more than one
cluster. The FCM clustering considers every object as a member of each cluster with a
variable degree of “membership” function. The similarity between the objects are evaluated
by using a distance measure that plays a crucial role in obtaining correct clusters. In each and
every iteration of FCM algorithm, the objective function is minimized that is mathematically
given in the Eq. (11).
∑ ∑ ‖ ‖ (11)
7. Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V. Madhusudhana Rao
http://www.iaeme.com/IJCIET/index.asp 388 editor@iaeme.com
Where, is represented as clusters, is denoted as data points, is stated as degree of
membership for the data point in cluster , and is represented as the centre vector of
cluster . The norm ‖ ‖ calculates the similarity of the data points to the centre vector
of cluster . For a given data , the degree of membership is calculated by using the Eq.
(12).
∑ (
‖ ‖
‖ ‖
)
(12)
Where, is denoted as the fuzziness coefficient and the center vector is calculated by
the Eq. (13).
∑
∑
(13)
In the Eq. (12) and (13), the fuzziness coefficient calculates the tolerance of the
clustering. The higher value of represents the larger overlap between the clusters. In
addition, the higher fuzziness coefficient utilizes a larger number of data points, where the
degree of membership is either one or zero. The degree of membership function evaluates the
iterations completed by the FCM algorithm. In this research study, the accuracy is measured
by using the degree of membership from one iteration to the next iteration , which is
calculated by the Eq. (14).
| | (14)
Where, is represented as the largest vector value, and are denoted as the degree
of membership of iterations and . The segmented LU and LC areas are graphically
denoted in the figures 4, 5 and 6. After segmentation, feature extraction is carried out for
extracting the feature vectors from the segmented regions.
Figure 4 Segmented image after using FCM algorithm (year; 2012)
8. Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
Adaptive Neuro-Fuzzy Inference System
http://www.iaeme.com/IJCIET/index.asp 389 editor@iaeme.com
Figure 5 Segmented image after using FCM algorithm (year; 2014)
Figure 6 Segmented image after using FCM algorithm (year; 2017)
3.4. Extracting the features from segmented satellite images
The feature extraction is defined as the action of mapping a satellite image from image space
to the feature space that converts large redundant data into a reduced data representation. In
this research study, feature extraction is performed on the basis of LBP and GLCM features.
The detailed description about the feature descriptors are given below.
3.4.1. Local binary pattern
The LBP is a texture analysis descriptor that converts a segmented satellite image into labels
based on the luminance value. Here, gray-scale invariance is an essential factor, which
depends on the local and texture patterns of a segmented image. In a satellite image , the pixel
position and radius are represented as , which are derived by using the central pixel
value of as the threshold to signify the neighbourhood pixel value . Further, the pixel
binary value is weighted using the power of two and then summed to produce a decimal
number for storing in the location of central pixel that is mathematically given in the Eq.
(15).
( ) ∑ ( ) ( ) * + (15)
Where, is represented as the gray level value of the central pixel of a local
neighbourhood. The basic neighbourhood LBP model is (p-neighbourhood), which gives
output that leads to a large number of possible patterns. The uniform model of LBP is
accomplished only when the jumping time is maximized. It is measured by using the Eq. (16).
9. Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V. Madhusudhana Rao
http://www.iaeme.com/IJCIET/index.asp 390 editor@iaeme.com
( ( )) | ( ) ( )| ∑ | ( ) ( )| (16)
Where, is represented as the maximum jumping time.
3.4.2. Gray-level co-occurrence matrix
In addition, a high level feature named as GLCM is employed for extracting the features of
segmented satellite image in order to differentiate the LU and LC areas. GLCM is the most
recognized texture analysis descriptor, which calculates the image characteristics associated
with second order statistics. The GLCM descriptor comprises of twenty-one features, in that
energy and homogeneity are considered in this research work for extracting the features from
a segmented satellite image. After extracting the feature vectors using LBP and GLCM
features. The obtained feature information is given as the input for an appropriate classifier:
ANFIS in order to perform classification.
3.4.2.1. Energy
The energy calculates the uniformity of normalized pixel pair distributions and also
determines the number of repeated pairs. Here, energy feature has a normalized value with the
maximum range of one. The higher energy value occurs only when the gray level distribution
has a periodic or constant form. Energy helps to reflect the depth and smoothness of the
satellite image texture structure. The formula to calculate the energy is given in the Eq. (17).
∑ ∑ ( ) (17)
3.4.2.2. Homogeneity
Homogeneity determines the closeness of distribution elements in the gray level matrix. To
quantitatively characterize the homogeneous texture regions for similarity, the local spatial
statistics of the texture is calculated using scale and orientation selective of Gabor filtering.
The segmented satellite image is subdivided into a set of homogeneous texture regions, then
the texture features are related to the regions of indexed image data. In GLCM, homogeneity
calculates four directions (i.e. = 0◦, 45◦, 90◦ or 135◦) with a feature vector size of four.
Homogeneity delivers high accuracy of detection in the defected areas, which are described
by a weak variation in grey level. The formula to calculate the homogeneity is represented in
the Eq. (18).
∑ ∑
( )
(18)
Where, is represented as the number of gray levels of satellite image, ( ) is denoted
as the pixel value of the position ( ) and is represented as the normalized co-occurrence
matrix.
3.5 Classification using ANFIS classifier
After obtaining the feature values, ANFIS classifier is used for classifying the patterns of a
satellite image. In this research, ANFIS classifier accomplishes multiple targets, because it is
more feasible and reliable compared to the individual target. ANFIS is a neuro-fuzzy model
that has the advantage of both neural networks and fuzzy logic. Initially, the learning process
is exploited on the extracted feature values ( ´ ) ( ´ ) ( ´ ). The basic rule of ANFIS
classifier is determined in the Eq. (19).
( ´ ) ( ´ ) ( ´ ) (19)
Where, are denoted as design parameters.
10. Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
Adaptive Neuro-Fuzzy Inference System
http://www.iaeme.com/IJCIET/index.asp 391 editor@iaeme.com
Layer 1;
In layer 1, every node is a square node with a node function. These node functions are
selected from the bell shaped curve with minimum 0 and maximum 1 value, which is given in
the Eq. (20).
( ´ ) ( ´ ) ( ´ )
( ´ ) ,(( )⁄ ) -
(20)
Where, are denoted as the parameter set and is stated as the degree of
membership functions for the fuzzy sets , and .
Layer 2;
In layer 2, every node is a circle node ∏ that multiplies the incoming values and send the
product out, which is mathematically represented in the Eq. (21).
( ´ ) ( ´ ) ( ´ ), (21)
Layer 3;
Here, every node is a circle node that evaluates the ratio of rules firing strength, which is
specified in the Eq. (22).
´
( )
(22)
Layer 4;
In layer 4, every node is a square node with a node function that is denoted in the Eq. (23).
´ (23)
Where, is represented as the output of layer 3.
Layer 5;
In this layer, all the incoming values are summarized and the overall output values are
denoted in the Eq. (24) and (25).
∑ ´ ∑ ´
∑ ´
(24)
´ ´ (25)
4. EXPERIMENTAL RESULT AND DISCUSSION
In the experimental phase, the proposed system was simulated by using MATLAB (version
2018a) with 3.0 GHZ-Intel i5 processor, 1TB hard disc, and 8 GB RAM. The proposed
system performance was related to other existing systems (Maximum likely hood algorithm
[16]) for estimating the effectiveness and efficiency of the proposed system. The proposed
system performance was validated by means of classification accuracy, sensitivity, and
specificity.
4.1. Performance measure
Performance measure is defined as the regular measurement of outcomes that develops a
reliable information about the efficiency and effectiveness of the proposed system. Also,
performance measure is the process of analyzing, collecting, and reporting information about
11. Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V. Madhusudhana Rao
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the performance of a group or individual. The mathematical formula of accuracy, sensitivity,
and specificity are denoted in the Eq. (26), (27), and (28).
(26)
(27)
(28)
Where, is denoted as false positive, is represented as true positive, is specified
as false negative, and is stated as true negative.
4.2 Quantitative analysis
In this segment, the LU and LC map was related to the reference data in order to calculate the
classification accuracy, sensitivity and specificity of the proposed system. In this research
paper, the reference data was prepared by considering the sample points of Google earth. The
obtained ground truth data helps in verifying the classification accuracy, sensitivity and
specificity of the proposed system. Here, the overall classification accuracy of proposed
system for the years of 2012, 2014 and 2017 are 95%, 92.75%, and 86.5%. Similarly, the
overall sensitivity of proposed system for the years of 2012, 2014 and 2017 are 93%, 87%,
and 97%. Correspondingly, the overall specificity of proposed system for the years of 2012,
2014 and 2017 are 98%, 92%, and 98%. The user’s value attains minimum specificity,
sensitivity and classification accuracy value, compared to the proposed system. The results of
the classification accuracy, sensitivity and specificity assessments are presented in the table 1.
The graphical comparison of accuracy, sensitivity and specificity are represented in the Fig. 7.
Table 1 Proposed system performance assessment report
LU and LC
classes
2012 2014 2017
Proposed
value
User’s value Proposed
value
User’s value Proposed
value
User’s value
Water-body 100% 100% 85% 87.67% 93% 90%
Vegetation 94% 90% 97% 100% 87% 82.5%
Settlement 96% 100% 89% 67.76% 88% 100%
Barren Land 90% 75% 100% 100% 78% 70%
Classification
accuracy
95% 91.25% 92.75% 88.85% 86.5% 85.625%
Sensitivity 93% 87% 87% 83% 97% 96%
Specificity 98% 73.34% 92% 80% 98% 90%
12. Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
Adaptive Neuro-Fuzzy Inference System
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Figure 7 Graphical comparison of classification accuracy, sensitivity and specificity
From the analysis of table 2, the settlement regions in Visakhapatnam city are increased
up to 8.99% from the year of 2012-1017, and the vegetation and water-body regions are
decreased up-to 1.89% and 8.41%. Hence, the increase in industrial areas and merchant
establishments are playing a major role in loss of agriculture areas. It is evaluated that the
Eutrophication phenomena are taking place in all the lakes and small water bodies, which
disappeared due to the indiscriminate dumping of solid waste and deposition of sediments in
Visakhapatnam city.
Table 2 Analysis report of Visakhapatnam city in terms of hectares (ha)
LU and LC classes 2012(ha) 2014(ha) 2017(ha) 2012-2017(ha)
Water-body 2512.83 2367.09 2301.32 -211.50 -8.41%
Vegetation 3629.62 3597.97 3561.01 -68.61 -1.89%
Settlement 33893.09 34764.13 36942.98 +3049.89 +8.99%
Barren Land 6851468.46 6850774.81 6848698.69 -2769.77 -0.04%
Total 6891504 6891504 6891504 - -
4.3. Comparative analysis
The comparative analysis of proposed and existing system is detailed in the table 3. M.
Harika, S.K. Aspiya Begum, S. Yamini, and K. Balakrishna, [16] developed an effective
system for LU and LC classification. In this research study, the satellite images were collected
for Visakhapatnam city in different time periods; 1988 and 2009. Then, histogram
equalization was performed on each image to improve the quality of the collected satellite
image. At last, maximum likely hood classifier was used for classifying the LU and LC
classes; built-up area, agricultural land, water bodies, barren area and shrubs. The developed
system almost achieved 83.35% of classification accuracy.
However, the proposed system achieved 91.42% of classification accuracy, which was
higher compared to the existing paper. In this research, the proposed system: FCM based
ANFIS algorithm extracts the both linear and non-linear characteristics of the satellite image
and also preserves the quantitative relationship between the extracted feature values. The
performance measures confirm that the proposed system performs effectively in LU and LC
13. Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V. Madhusudhana Rao
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classification in light of classification accuracy, sensitivity, and specificity. The efficiency
and effectiveness of the proposed system are represented in the tables 1 and 3.
Table 3 Comparative analysis of proposed and existing system
5. CONCLUSION
The main goal of this research work is to provide an effective supervised system for
classifying the LU and LC classes. The proposed system helps the research analysts in under-
standing the environmental changes for ensuring the sustainable development, especially for
Visakhapatnam city. In this scenario, HDL technique was applied to remove the saturation
and blooming effects in the input satellite images. The denoised satellite images were given as
the input for FCM clustering for segmenting the LU and LC areas. Then, hybrid feature
extraction (LBP and GLCM (Homogeneity and energy)) was employed for extracting the
feature values. These feature values were classified by using the classifier: ANFIS. Compared
to other existing systems in LU and LC classification, the proposed system achieved a
superior performance, which showed 7% of improvement in classification accuracy. In future
work, a new unsupervised system was developed for analyzing the LU and LC classes for
other metropolitan cities in India.
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Methodology Study area Classification accuracy (%)
Maximum likely hood
algorithm [16]
Visakhapatnam 83.35
ANFIS Visakhapatnam 91.42
14. Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
Adaptive Neuro-Fuzzy Inference System
http://www.iaeme.com/IJCIET/index.asp 395 editor@iaeme.com
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