Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectro-temporal data for land cover classification
Deep learning (DL) techniques are effective in various applications, such as parameter estimation, image classification, recognition, and anomaly detection. They excel with abundant training data but struggle with limited data. To overcome this, transfer learning is commonly used, leveraging complex learning abilities, saving time, and handling limited labeled data. This study assesses a transfer learning (TL)-based pre-trained “deep convolutional neural network (DCNN)” for classifying land use land cover using a limited and imbalanced dataset of fused spectro-temporal data. It compares the performance of shallow artificial neural networks (ANNs) and deep convolutional neural networks, utilizing multi-spectral sentinel-2 and high-resolution planet scope data. Both machine learning and deep learning algorithms successfully classified the fused data, but the transfer learning-based deep convolutional neural network outperformed the artificial neural network. The evaluation considered a weighted average of F1-score and overall classification accuracy. The transfer learning-based convolutional neural network achieved a weighted average F1-score of 0.92 and a classification accuracy of 0.93, while the artificial neural network achieved a weighted average F1-score of 0.87 and a classification accuracy of 0.89. These results highlight the superior performance of the transfer learned convolutional neural network on a limited and imbalanced dataset compared to the traditional artificial neural network algorithm.
Multi-scale 3D-convolutional neural network for hyperspectral image classific...nooriasukmaningtyas
Deep Learning methods are state-of-the-art approaches for pixel-based hyperspectral images (HSI) classification. High classification accuracy has been achieved by extracting deep features from both spatial-spectral channels. However, the efficiency of such spatial-spectral approaches depends on the spatial dimension of each patch and there is no theoretically valid approach to find the optimum spatial dimension to be considered. It is more valid to extract spatial features by considering varying neighborhood scales in spatial dimensions. In this regard, this article proposes a deep convolutional neural network (CNN) model wherein three different multi-scale spatial-spectral patches are used to extract the features in both the spatial and spectral channels. In order to extract these potential features, the proposed deep learning architecture takes three patches various scales in spatial dimension. 3D convolution is performed on each selected patch and the process runs through entire image. The proposed is named as multi-scale three-dimensional convolutional neural network (MS-3DCNN). The efficiency of the proposed model is being verified through the experimental studies on three publicly available benchmark datasets including Pavia University, Indian Pines, and Salinas. It is empirically proved that the classification accuracy of the proposed model is improved when compared with the remaining state-of-the-art methods.
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.
An Efficient K-Nearest Neighbors Based Approach for Classifying Land Cover Re...IDES Editor
In recent times, researchers in the remote
sensing community have been greatly interested in
utilizing hyperspectral data for in-depth analysis of
Earth’s surface. In general, hyperspectral imaging comes
with high dimensional data, which necessitates a pressing
need for efficient approaches that can effectively process
on these high dimensional data. In this paper, we present
an efficient approach for the analysis of hyperspectral
data by incorporating the concepts of Non-linear manifold
learning and k-nearest neighbor (k-NN). Instead of
dealing with the high dimensional feature space directly,
the proposed approach employs Non-linear manifold
learning that determines a low-dimensional embedding of
the original high dimensional data by computing the
geometric distances between the samples. Initially, the
dimensionality of the hyperspectral data is reduced to a
pairwise distance matrix by making use of the Johnson's
shortest path algorithm and Multidimensional scaling
(MDS). Subsequently, based on the k-nearest neighbors,
the classification of the land cover regions in the
hyperspectral data is achieved. The proposed k-NN based
approach is evaluated using the hyperspectral data
collected by the NASA’s (National Aeronautics and Space
Administration) AVIRIS (Airborne Visible/Infrared
Imaging Spectrometer) from Kennedy Space Center,
Florida. The classification accuracies of the proposed k-
NN based approach demonstrate its effectiveness in land
cover classification of hyperspectral data.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Land scene classification from remote sensing images using improved artificia...IJECEIAES
The images obtained from remote sensing consist of background complexities and similarities among the objects that act as challenge during the classification of land scenes. Land scenes are utilized in various fields such as agriculture, urbanization, and disaster management, to detect the condition of land surfaces and help to identify the suitability of the land surfaces for planting crops, and building construction. The existing methods help in the classification of land scenes through the images obtained from remote sensing technology, but the background complexities and presence of similar objects act as a barricade against providing better results. To overcome these issues, an improved artificial bee colony optimization algorithm with convolutional neural network (IABC-CNN) model is proposed to achieve better results in classifying the land scenes. The images are collected from aerial image dataset (AID), Northwestern Polytechnical University-Remote Sensing Image Scene 45 (NWPU-RESIS45), and University of California Merced (UCM) datasets. IABC effectively selects the best features from the extracted features using visual geometry group-16 (VGG-16). The selected features from the IABC are provided for the classification process using multiclass-support vector machine (MSVM). Results obtained from the proposed IABC-CNN achieves a better classification accuracy of 96.40% with an error rate 3.6%.
Multi-scale 3D-convolutional neural network for hyperspectral image classific...nooriasukmaningtyas
Deep Learning methods are state-of-the-art approaches for pixel-based hyperspectral images (HSI) classification. High classification accuracy has been achieved by extracting deep features from both spatial-spectral channels. However, the efficiency of such spatial-spectral approaches depends on the spatial dimension of each patch and there is no theoretically valid approach to find the optimum spatial dimension to be considered. It is more valid to extract spatial features by considering varying neighborhood scales in spatial dimensions. In this regard, this article proposes a deep convolutional neural network (CNN) model wherein three different multi-scale spatial-spectral patches are used to extract the features in both the spatial and spectral channels. In order to extract these potential features, the proposed deep learning architecture takes three patches various scales in spatial dimension. 3D convolution is performed on each selected patch and the process runs through entire image. The proposed is named as multi-scale three-dimensional convolutional neural network (MS-3DCNN). The efficiency of the proposed model is being verified through the experimental studies on three publicly available benchmark datasets including Pavia University, Indian Pines, and Salinas. It is empirically proved that the classification accuracy of the proposed model is improved when compared with the remaining state-of-the-art methods.
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.
An Efficient K-Nearest Neighbors Based Approach for Classifying Land Cover Re...IDES Editor
In recent times, researchers in the remote
sensing community have been greatly interested in
utilizing hyperspectral data for in-depth analysis of
Earth’s surface. In general, hyperspectral imaging comes
with high dimensional data, which necessitates a pressing
need for efficient approaches that can effectively process
on these high dimensional data. In this paper, we present
an efficient approach for the analysis of hyperspectral
data by incorporating the concepts of Non-linear manifold
learning and k-nearest neighbor (k-NN). Instead of
dealing with the high dimensional feature space directly,
the proposed approach employs Non-linear manifold
learning that determines a low-dimensional embedding of
the original high dimensional data by computing the
geometric distances between the samples. Initially, the
dimensionality of the hyperspectral data is reduced to a
pairwise distance matrix by making use of the Johnson's
shortest path algorithm and Multidimensional scaling
(MDS). Subsequently, based on the k-nearest neighbors,
the classification of the land cover regions in the
hyperspectral data is achieved. The proposed k-NN based
approach is evaluated using the hyperspectral data
collected by the NASA’s (National Aeronautics and Space
Administration) AVIRIS (Airborne Visible/Infrared
Imaging Spectrometer) from Kennedy Space Center,
Florida. The classification accuracies of the proposed k-
NN based approach demonstrate its effectiveness in land
cover classification of hyperspectral data.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Land scene classification from remote sensing images using improved artificia...IJECEIAES
The images obtained from remote sensing consist of background complexities and similarities among the objects that act as challenge during the classification of land scenes. Land scenes are utilized in various fields such as agriculture, urbanization, and disaster management, to detect the condition of land surfaces and help to identify the suitability of the land surfaces for planting crops, and building construction. The existing methods help in the classification of land scenes through the images obtained from remote sensing technology, but the background complexities and presence of similar objects act as a barricade against providing better results. To overcome these issues, an improved artificial bee colony optimization algorithm with convolutional neural network (IABC-CNN) model is proposed to achieve better results in classifying the land scenes. The images are collected from aerial image dataset (AID), Northwestern Polytechnical University-Remote Sensing Image Scene 45 (NWPU-RESIS45), and University of California Merced (UCM) datasets. IABC effectively selects the best features from the extracted features using visual geometry group-16 (VGG-16). The selected features from the IABC are provided for the classification process using multiclass-support vector machine (MSVM). Results obtained from the proposed IABC-CNN achieves a better classification accuracy of 96.40% with an error rate 3.6%.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
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
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
Multispectral images are used for space Arial application, target detection and remote sensing application. MS images are very rich in spectral resolution but at a cost of spatial resolution. We propose a new method to increase a spatial resolution MS images. For spatial resolution enhancement of MS images we need to employ a super-resolution technique which uses a Principal Component Analysis (PCA) based approach by learning an edge details from database. Experiments have been carried out on both real multispectral (MS) data and MS data. This experiment is done with the usefulness for hyper spectral (HS) data as a future work.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
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.
Masking preprocessing in transfer learning for damage building detectionIAESIJAI
The sudden climate change occurring in different places in the world has
made disasters more unpredictable than before. In addition, responses are
often late due to manual processes that have to be performed by experts.
Consequently, major advances in computer vision (CV) have prompted
researchers to develop smart models to help these experts. We need a strong
image representation model, but at the same time, we also need to prepare
for a deep learning environment at a low cost. This research attempts to
develop transfer learning models using low-cost masking pre-processing in
the experimental building damage (xBD) dataset, a large-scale dataset for
advancing building damage assessment. The dataset includes eight types of
disasters located in fifteen different countries and spans thousands of square
kilometers of satellite images. The models are based on U-Net, i.e., AlexNet,
visual geometry group (VGG)-16, and ResNet-34. Our experiments show
that ResNet-34 is the best with an F1 score of 71.93%, and an intersection
over union (IoU) of 66.72%. The models are built on a resolution of 1,024
pixels and use only first-tier images compared to the state-of-the-art
baseline. For future orientations, we believe that the approach we propose
could be beneficial to improve the efficiency of deep learning training.
Detection of urban tree canopy from very high resolution imagery using an ob...IJECEIAES
Tree that grows within a town, city and suburban areas, collection of these trees makes the urban forest. These urban forest and urban trees have impact on urban water, pollution and heat. Nowadays we are experiencing drastic climatic changes because of cutting of trees for our growth and increasing population which leads to expansion of roads, towers, and airports. Individual tree crown detection is necessary to map the forest along with feasible planning for urban areas. In this study, using WorldView-2imagery, trees in specific area are detected with object-based image analysis (OBAI) approach. Therefore with improvement in spatial and spectral resolution of an image, extracted from WorldView-2 carried out urban features with better accuracy. The aim of this research is to illustrate how object-based method can be applied to the available data to accurately find out vegetation, which can be further sub-classified to obtain area under tree canopy. The result thus obtained gives area under tree canopy with an accuracy of 92.43 % and a Kappa coefficient of 0.80.
Wide-band spectrum sensing with convolution neural network using spectral cor...IJECEIAES
Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
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Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
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
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
Multispectral images are used for space Arial application, target detection and remote sensing application. MS images are very rich in spectral resolution but at a cost of spatial resolution. We propose a new method to increase a spatial resolution MS images. For spatial resolution enhancement of MS images we need to employ a super-resolution technique which uses a Principal Component Analysis (PCA) based approach by learning an edge details from database. Experiments have been carried out on both real multispectral (MS) data and MS data. This experiment is done with the usefulness for hyper spectral (HS) data as a future work.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
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.
Masking preprocessing in transfer learning for damage building detectionIAESIJAI
The sudden climate change occurring in different places in the world has
made disasters more unpredictable than before. In addition, responses are
often late due to manual processes that have to be performed by experts.
Consequently, major advances in computer vision (CV) have prompted
researchers to develop smart models to help these experts. We need a strong
image representation model, but at the same time, we also need to prepare
for a deep learning environment at a low cost. This research attempts to
develop transfer learning models using low-cost masking pre-processing in
the experimental building damage (xBD) dataset, a large-scale dataset for
advancing building damage assessment. The dataset includes eight types of
disasters located in fifteen different countries and spans thousands of square
kilometers of satellite images. The models are based on U-Net, i.e., AlexNet,
visual geometry group (VGG)-16, and ResNet-34. Our experiments show
that ResNet-34 is the best with an F1 score of 71.93%, and an intersection
over union (IoU) of 66.72%. The models are built on a resolution of 1,024
pixels and use only first-tier images compared to the state-of-the-art
baseline. For future orientations, we believe that the approach we propose
could be beneficial to improve the efficiency of deep learning training.
Detection of urban tree canopy from very high resolution imagery using an ob...IJECEIAES
Tree that grows within a town, city and suburban areas, collection of these trees makes the urban forest. These urban forest and urban trees have impact on urban water, pollution and heat. Nowadays we are experiencing drastic climatic changes because of cutting of trees for our growth and increasing population which leads to expansion of roads, towers, and airports. Individual tree crown detection is necessary to map the forest along with feasible planning for urban areas. In this study, using WorldView-2imagery, trees in specific area are detected with object-based image analysis (OBAI) approach. Therefore with improvement in spatial and spectral resolution of an image, extracted from WorldView-2 carried out urban features with better accuracy. The aim of this research is to illustrate how object-based method can be applied to the available data to accurately find out vegetation, which can be further sub-classified to obtain area under tree canopy. The result thus obtained gives area under tree canopy with an accuracy of 92.43 % and a Kappa coefficient of 0.80.
Wide-band spectrum sensing with convolution neural network using spectral cor...IJECEIAES
Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
Similar to Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectro-temporal data for land cover classification (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Fuzzy logic method-based stress detector with blood pressure and body tempera...IJECEIAES
In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decisionmaker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
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
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
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Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectro-temporal data for land cover classification
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6882~6890
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6882-6890 6882
Journal homepage: http://ijece.iaescore.com
Performance evaluation of transfer learning based deep
convolutional neural network with limited fused spectro-
temporal data for land cover classification
Muhammad Hasanat1,2
, Waleed Khan1,2
, Nasru Minallah1,2
, Najam Aziz1,2
, Awab-Ur-Rashid Durrani1,2
1
Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Khyber Pakhtoonkhwa, Pakistan
2
National Center of Big data and Cloud Computing, University of Engineering and Technology Peshawar, Peshawar, KP Pakistan
Article Info ABSTRACT
Article history:
Received Nov 11, 2022
Revised Apr 20, 2023
Accepted Apr 24, 2023
Deep learning (DL) techniques are effective in various applications, such as
parameter estimation, image classification, recognition, and anomaly
detection. They excel with abundant training data but struggle with limited
data. To overcome this, transfer learning is commonly used, leveraging
complex learning abilities, saving time, and handling limited labeled data.
This study assesses a transfer learning (TL)-based pre-trained “deep
convolutional neural network (DCNN)” for classifying land use land cover
using a limited and imbalanced dataset of fused spectro-temporal data. It
compares the performance of shallow artificial neural networks (ANNs) and
deep convolutional neural networks, utilizing multi-spectral sentinel-2 and
high-resolution planet scope data. Both machine learning and deep learning
algorithms successfully classified the fused data, but the transfer learning-
based deep convolutional neural network outperformed the artificial neural
network. The evaluation considered a weighted average of F1-score and
overall classification accuracy. The transfer learning-based convolutional
neural network achieved a weighted average F1-score of 0.92 and a
classification accuracy of 0.93, while the artificial neural network achieved a
weighted average F1-score of 0.87 and a classification accuracy of 0.89. These
results highlight the superior performance of the transfer learned
convolutional neural network on a limited and imbalanced dataset compared
to the traditional artificial neural network algorithm.
Keywords:
Artificial neural network
Deep learning
Neural networks
Remote sensing
Transfer learning
This is an open access article under the CC BY-SA license.
Corresponding Author:
Nasru Minallah
Department of Computer Systems Engineering, University of Engineering and Technology Peshawar
Peshawar, KP, Pakistan
Email: n.minallah@uetpeshawar.edu.pk
1. INTRODUCTION
In recent times, the field of deep learning has gained immense popularity and has become a central
topic in big data research due to its superior performance compared to traditional machine learning algorithms.
This has resulted in its successful application in a variety of fields, such as image identification [1], natural
language processing [2], and speech enhancement [3]. The use of deep learning models in remotely sensed
images has also become increasingly popular, with models such as ResNet, AlexNet, and capsule network
exhibiting remarkable performance when trained with ample labelled data. However, creating large-scale, well-
labelled datasets for remote sensing is challenging due to the high cost associated with data collection and
annotation [4]. Furthermore, the increasing availability of large amounts of data from advanced satellite sensors
has led to newly collected remote sensing data often lacking labelled information, posing a challenge to deep
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Performance evaluation of transfer learning based deep convolutional neural … (Muhammad Hasanat)
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learning models. To address this issue, transfer learning-based frameworks have been developed, which use
existing labelled remote sensing data to supplement the new, unlabeled data [5]. Transfer learning [6] is a
technique inspired by the human tendency to apply prior experience to new activities, and it involves training
a neural network model on a problem like the one being solved, leading to a reduction in training time and
lowering generalization error [7]. The success of deep learning models heavily depends on the availability of
ground truth training data, which is not always accessible. Therefore, researchers have developed transfer
learning, which involves the use of finely-tuned pre-trained models, as seen in applications such as crop yield
prediction in regions with limited training data. This manuscript focuses on evaluating and investigating the
performance of a transfer learning-based convolutional neural network (TL-CNN) [8] pre-trained on spectro-
temporal satellite data for remote-sensing scene classification over a limited imbalanced dataset. Additionally,
a shallow artificial neural network and a machine learning algorithm trained from scratch exclusively on the
new dataset are also evaluated.
Deep learning has been increasingly popular in recent years across a variety of academic and
professional fields, particularly in computer vision [9]. Convolutional neural networks (CNNs) [10] have
demonstrated extraordinary success in a variety of applications, including speech and object recognition [11].
One such model is the AlexNet, introduced by Krizhevsky et al. [1], which has played a vital role in the
widespread adoption of deep learning in computer vision. CNNs are currently the leading approach in many
image-related tasks, including image classification, segmentation, and detection, and have achieved outstanding
results in benchmarks such as the Modified National Institute of Standards and Technology (MNIST) hand-
written database and the ImageNet dataset [12], [13] which comprises billions of natural images.
However, training CNNs with limited data can be challenging despite their advanced feature-
extraction capabilities [14]. Yin et al. [15] and Yosinski et al. [16] discovered that CNN models trained on
diverse sets of images tend to learn parameters in a common pattern, where layers closer to the input learn
generic features, while those farther from the input learn more specific features relevant to the dataset. This
finding led to the development of transfer learning, which involves using filters learned by a CNN during
primary tasks for unrelated secondary tasks [17], [18]. The primary CNN model can also be used to extract
features and serve as a foundation for the secondary task.
Despite the fact that huge datasets have the ability to significantly enhance CNN performance, transfer
learning has made it possible to use CNN in scientific disciplines with limited data. For example, Lima et al.
[19] used transfer learning to identify photos of drill cores from oil fields, Lima et al. [20] used it to categorize
Herbarium specimens, Lima et al. [20] used it to classify diverse geo-science images, and so on. Razavian
et al. [21], used transfer learning to research a variety of recognition tasks, including item picture classification,
scene identification, and image retrieval, while Duarte-Coronado et al. [22] used it to detect porosity in thin
section images. Remote sensing has also made substantial use of transfer learning.
2. STUDY AREA
The Agriculture University's research farm in Peshawar, Pakistan, was taken into consideration for
the experimental setup. The region covers about 125 hectares of area and is regarded as experimental grounds
for a variety of vegetation. The targeted pilot region illustrated in Figure 1 is a small area consisting of
numerous types of agricultural crops, such as wheat, mustard, clover and also the urban class.
Figure 1. Locality map of agricultural farm, University of Peshawar
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2.1. Data description
Data utilized in our research is the multi-spectral sentinel-2 and planet scope data. Multi-spectral
sentinel-2 is acquired from Copernicus open hub while high resolution planet data is acquired from Planet Inc
through a research grant. The European satellite sentinel-2A was launched in June 2015. It has a multispectral
sensors that can produce images in thirteen different spectral bands, from visible to short-wave infrared [23].
Whereas, California-based private company Planet Inc. provides satellite imaging services, launched its first
dove nano satellite in 2015. Its spatial resolution in ISS orbit ranges from 2.7 to 3.2 m, while 3.7 to 4.9 m in
Sun Synchronous. With a one-day temporal resolution, it gives geo analysts access to 4 channels with global
coverage, comprising red, green, and blue (RGB) and near infrared reflectance (NIR) [24].
3. METHOD
Figure 2 illustrates the flow diagram of the adopted methodology. In order to capture the temporal
information along with spectral surface reflectance values over the pilot region, sentinel-2 data was acquired
on the 3rd
of February, the 18th
of February, the 19th
of March, and the 23rd
of April, whereas, a single planet
scope scene was acquired on 29th
March. After calculating normalized difference vegetation indices (NDVI)
for each image, the acquired data are then layer stacked and fused into a single image comprising of four bands
(red, green, NIR and NDVI) each, and accumulating to twenty overall bands.
Figure 2. Data-flow diagram of experimental setup
In the specified pilot region, a survey was performed to gather ground truth data. We used our native
Android based Geo Survey application to conduct the survey. During our survey eight different categories were
identified over the pilot region.
3.1. Ground truth data
Survey was conducted to gather the ground truth data used in classification of crops in the region of
interest using Geo Survey application. Collected field samples are mentioned in Table 1. 8 landcover types
were collected during this activity which include wheat, urban, turnip, clover, oats, mustard, chickpea and
barley. Our indigenously developed mobile survey application “Geo Survey” was used for this purpose. This
application is freely available in both Android and iOS.
Table 1. Vegetation categorization in training data
S. No. 1 2 3 4 5 6 7 8
Class Wheat Urban Turnip Clover Oats Mustard Chick-Pea Barley
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Performance evaluation of transfer learning based deep convolutional neural … (Muhammad Hasanat)
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The free Geo Survey application, which is accessible from the Google Play Store [25] is used to collect
training data. Training dataset is made up of 70% of the data from the ground truth survey, while the remaining
30% is used for testing. The fully connected layers of the TL-CNN as well as the artificial neural networks
(ANN) model algorithms are trained using the training data. In contrast, 30% of the test set data are used to
validate the models' performance.
3.2. Artificial neural networks
In our experiment, we used an ANN, which is a kind of machine learning algorithm created to
resemble the human cognitive system. Figure 3(a) shows the input layer, output layer, and hidden layer as the
three layers that make up the ANN model. We performed non-linear classification and carried out supervised
learning using a multi-layered feed-forward ANN with back propagation. The complexity of the features that
neural networks learn from their input data varies with the depth of the networks.
However, as we have limited data, therefore we have not employed depth layers, rather a single hidden
layer is used in neural network architecture. The implementation of the model and its utilization over a limited
labeled dataset is due to the fact that usually machine learning models works best in comparison to deep
learning algorithms over limited data. The model architecture of the ANN having spectro-temporal features at
input is depicted in Figure 3(a). The activation function receives the sum of the input values. The network
provides feedback once the input data has been processed, and if an error has occurred, it is fixed by recursively
adjusting the network weights [26]. Performance of the classifier is analyzed while adjusting various
parameters and weights of the interconnected nodes.
How much internal weight has affected the node's level of activation is determined by training
activation threshold settings. The size of the weight change for certain nodes can be estimated with good
accuracy using training rate. The training momentum parameter prevents convergence of the system to a local
minimum. To stop the algorithm from obtaining additional training, training root-mean-square (RMS) Exit
criteria are utilized. Mathematical calculations are needed to uncover hidden layers. The number of training
iterations decides how many more iterations the algorithm should undergo training and the min output
activation threshold establishes the threshold value below which a pixel is categorized as unclassified.
Figure 3(b) displays the values utilized for various ANN parameters.
Parameters Values
Training activation threshold 0.9
Training rate 0.1
Training momentum 0.9
Training RMS exit criteria 0.1
Number of hidden layers 1
Number of training iterations 200
Min output activation threshold 0.07
(a) (b)
Figure 3. Illustration of input data structure and parameter selection of deep neural networks (a) ANNs with
temporal features and (b) parameter selection for ANNs
3.3. Transfer learning based deep convolutional neural network
The deep convolutional neural network inspired by inception, as proposed by Minallah et al. [27] was
the basis for the deep learning model that we aimed to build. Three temporal convolutional inception blocks, a
dense layer, and a softmax layer are included in the original convolutional neural network model that was
inspired by Inception. These layers are used to classify data into many categories. Parallel filters of sizes 1, 3,
and 5 are utilized to create feature maps in each of the temporal convolutional inception blocks, which are then
combined and sent on to the following layer. In a single temporal convolutional inception block, there are 32
filter units overall for each of the three filter sizes (1, 3, and 5), which makes it a total of 96 units. These blocks
which employs 1D convolutional filters, are reason behind spectro-temporal feature learning. The convolutional
filter already demonstrated their effectiveness in learning temporal characteristics [24]. The referenced model
takes spectro-temporal features as input and predict the output class.
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In our proposed transfer learning-based inception inspired CNN model presented in Figure 4, the
model receives a 4×5-2D matrix as its input, where the row represents spectral bands, while the columns are
timesteps (𝑡1,𝑡2, 𝑡3, 𝑡4, 𝑡5) on which the images are acquired. Thus, the same pixel from numerous dates is
stacked in a matrix column. In order to initialize the network, the pre-trained weights from the spectro-temporal
data were employed after the convolutional block of the original network design of the Inception-inspired deep
CNN was frozen. The initialized weights were then changed during the fine-tuning process so that the network
could learn the specific features of the new task with limited unbalanced input. The last layers of the original
model were modified with inclusion of fully connected layers in proposed model. This modified model is then
trained from scratch on the limited imbalanced Spectro-temporal data for different classification classes with
fine tuning of various hyper-parameters. Figures 5(a) and 5(b) shows the loss and accuracy of the TL-CNN
model over hundred epochs.
For the purpose of classifying the study area, we used both our ANN and TL-CNN models to examine
the raster multispectral remote sensing data that has been merged spectro-temporally. The results were then
used to generate classified landcover maps of the pilot region, as depicted in Figure 6. Figure 6(a) presents
classified results generated through ANN while Figure 6(b) shows classified map for TL-CNN. The results of
transfer learning-based CNN implemented model are explained in results and discussion section.
Figure 4. Transfer learning based DCNN
(a) (b)
Figure 5. Model loss and model accuracy of the proposed model (a) model loss for train and test data for
TL-CNN and (b) model accuracy for train and test data for TL-CNN
2.4. Accuracy Assessment
Performance of the models is measured by class-wise precision, recall, F1-score, and a weighted
average of F1-score and overall accuracy [28]. Due to the unbalanced structure of the dataset, the weighted
average of the F1-score is given special attention for the evaluation of the overall performance of the classifiers.
Equations (1) to (4) are used to determine the precision, recall, and F1-score.
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𝑅𝑒𝑐𝑎𝑙𝑙 =
(𝑡𝑝)
(𝑡𝑝+𝑓𝑛)
(1)
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
(𝑡𝑝)
(𝑡𝑝+𝑓𝑝)
(2)
𝐹1 − 𝑆𝑐𝑜𝑟𝑒 = (2 ∗
𝑅𝑒𝑐𝑎𝑙𝑙∗𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛
𝑅𝑒𝑐𝑎𝑙𝑙+𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛
) (3)
𝑂𝑣𝑒𝑟𝑎𝑙𝑙 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = (
(𝑡𝑝+𝑡𝑛)
(𝑡𝑝+𝑓𝑛+𝑡𝑛+𝑓𝑝)
) (4)
In (1) to (4), tp, fp, fn, and tn, respectively, stand for true positive, false positive, false negative, and true negative.
(a) (b)
Figure 6. Land cover land use map generated through (a) ANN classified map and (b) TL-CNN
classified map
4. RESULTS AND DISCUSSION
The effectiveness of the current artificial neural network and the suggested deep convolutional neural
network based on transfer learning will be discussed in this section. Neural networks are incredibly effective
algorithms that can be used to learn a non-linear relationship between input and desired output. The features’
degree of complexity learned from the input data relates to the depth of neural networks. However, as we have
limited data, therefore we have not employed depth layers, rather a single hidden layer is used in the neural
network architecture. In Table 2, the classification report of the ANN model, shows that the ANN achieved
0.87 weighted average of F1-score, while classification accuracy is 0.89. The weighted average better
approximates the model performance as the dataset used is imbalanced in nature. From the same table, it can
also be seen that overall performance of the model in precision and recall is satisfactory, as the model achieved
0.84 and 0.86 weighted average respectively. However, for individual classes where the training data is
comparatively less such as Turnip, Chickpea and Barley, the model does not perform well, as can be seen in
Figures 7(a) and 7(b). Although, the urban class is also having less data, but it performed well with 0.94 and 1
precision and recall score. This is because that the urban class has different spectral reflectance, as compared
to other limited data classes. The Turnip, Chickpea, and Barley classes degraded performance is because of
limited training data as well the spectral overlap between these classes, which is confusing for the model to
learn. For all other classes the performance of the ANN is quite satisfactory. The classification report of our
implemented transfer learning-based CNN model on test set is shown in Table 3. Which shows that our
TL-CNN model achieved 0.92 weighted average of F1-score and an overall classification accuracy of 0.93.
The model has a good overall performance with limited data and an uneven class distribution. All the classes
having similarity with the pre-trained model dataset have achieved good precision, recall and F1-score.
However, the performance of the classes having different spectral reflectance values than the original pre-
trained model dataset, spectral similarity among themselves and also have limited number of samples are not
good. The F1-score for the Chickpea class is 0.71, the least among all the classes, followed by Barley and
Turnip classes, which are 0.82 and 0.83 respectively. The precision and recall score for both algorithms over
individual classes is illustrated in Figures 7(a) and 7(b). The difference in support pixels of both these models
is due to the fact that ANN has included some of the pixels of different classes into unclassified group, which
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is omitted. However, can be seen in ANN classified map Figure 6. The overall performance of both the models
by weighted average of F1-score and classification accuracy can be seen in Figure 7(c).
Table 2. Simulated results of ANN classifier
Classes Precision Recall F1-score Support
Wheat 0.92 0.97 0.94 189
Urban 0.94 0.100 0.97 19
Turnip 0.100 0.37 0.54 18
Clover 0.97 0.96 0.97 262
Oats 0.84 0.93 0.88 111
Mustard 0.86 0.79 0.82 58
Chick Pea 0.40 0.80 0.53 10
Barley 0.100 0.57 0.73 11
Accuracy 0.89 697
Macro Avg 0.64 0.68 0.79 697
Weighted Avg 0.84 0.86 0.87 697
Classification Accuracy: 0.89
Table 3. Classification results of CNN
Classes Precision Recall F1-score Support
Wheat 0.93 0.94 0.93 191
Urban 0.95 0.100 0.97 18
Turnip 0.83 0.83 0.83 35
Clover 0.97 0.97 0.97 265
Oats 0.95 0.89 0.92 101
Mustard 0.87 0.95 0.91 63
Chick Pea 0.56 0.100 0.71 5
Barley 0.93 0.74 0.82 19
Accuracy 0.93 697
Macro Avg 0.87 0.91 0.88 697
Weighted Avg 0.94 0.93 0.92 697
Classification Accuracy: 0.93
(a) (b)
(c)
Figure 7. Precision, recall and overall accuracy of the employed models as seen in (a) graphical comparison
of ANN and TL-CNN through precision, (b) graphical comparison of ANN and TL-CNN classified map,
and (c) graphical comparison of F1 score and accuracy
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5. CONCLUSION
In this study, the performance of a shallow artificial neural network algorithm and a convolutional
neural network based on transfer learning were compared. It is determined from the experiments and findings
that limited data do impact the performance of the model, however the variation in the spectral reflectance
values of the various classes is more important for class distinction and model performance. Furthermore, it is
also concluded that using a relatively small dataset with a machine learning model can yield satisfactory results
for classes having varying spectral reflectance values, however the advantages of a transfer learning based
pretrained deep convolutional neural network may be observed in this use scenario.
ACKNOWLEDGEMENTS
The project was carried out under the umbrella of National Center of Big Data and Cloud Computing
(NCBC), University of Engineering and Technology, Peshawar, under the auspices of Higher Education
Commission, Pakistan. We are greatly thankful to Planet INC. for their data grant which made this research possible.
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BIOGRAPHIES OF AUTHORS
Muhammad Hasanat received his BSc degree in Electrical Engineering from the
University of Engineering and Technology, Peshawar, Pakistan. He is pursuing his MSc degree
from the Department of Computer Systems Engineering at UET Peshawar. His primary research
interest are machine learning and deep learning algorithms in Remote Sensing. He can be
contacted at email imhasanat007@yahoo.com.
Waleed Khan received his B.Sc. degree in Electrical Computer Engineering from
Comsats University Islamabad, Abbottabad Campus Pakistan in 2015. The MSc Degree in
Computer Systems Engineering from the University of Engineering and Technology, Peshawar
(UET Peshawar), Pakistan. He is pursuing his Ph.D. from the Department of Computer Systems
Engineering at UET Peshawar. His main area of research involves deep learning algorithms in
Remote Sensing. He can be contacted at email khanwaleed@uetpeshawar.edu.pk.
Nasru Minallah received the B.Sc. degree in computer engineering from the UET,
Peshawar, Pakistan, in 2004 and the M.Sc. degree in computer engineering from LUMS, Lahore,
Pakistan, in 2006 and Ph.D. degree from the Communications Group, School of Electronics and
Computer Science, University of Southampton, Southampton, U.K. in 2010. Furthermore, he
has been a postdoctoral research fellow in France Telecom, Orange Lab, Paris. He is currently
working as Associate Professor in Department of Computer Systems Engineering, University
of Engineering and Technology Peshawar, Pakistan. His research interests include remote
sensing, image processing, low-bit-rate video coding for wireless communications, turbo
coding and detection, and iterative source-channel decoding. He can be contacted at email
n.minallah@uetpeshawar.edu.pk.
Najam Aziz has received his B.Sc and M.Sc Degrees in computer systems
Engineering from University of Engineering and Technology (UET) Peshawar, Pakistan. He is
pursuing his Doctorate degree from the Department of Computer Systems Engineering at UET
Peshawar. Currently, he is working as a Research Assistant at National Center for Big Data and
Cloud Computing (NCBC) at UET Peshawar, Pakistan. He is having research interests in data
science, artificial intelligence and deep learning. His main area of research involves deep
learning algorithms in the field of remote sensing and medical imaging. He can be contacted at
email najamkhattak@gmail.com.
Awab-Ur-Rashid Durrani received the B.Sc. and M.Sc. degrees in electrical
engineering from Ain Shams University, Cairo, Egypt, in 1985 and 1990, respectively, and the
Ph.D. degree in electrical engineering according to the channel system between Ain Shams
University, Egypt, and Brunel University, U.K., in 1996. He has been a professor of electrical
power engineering with Ain Shams University, since 2007. He is currently the Vice Dean of
Faculty of Engineering and Technology, Future University in Egypt, Cairo, Egypt. He has authored
or coauthored more than 400 refereed journal and conference papers, 25 book chapters, and three
edited books with Elsevier and Springer. His research interests include the applications of artificial
intelligence, evolutionary and heuristic optimization techniques to power system planning,
operation, and control. He can be contacted at email awabkhan.cse@uetpeshawar.edu.pk.