Pub Date : 2023-01-01DOI: 10.14311/nnw.2023.33.020
Enrico Pozzobon, Nils Weiß, Jürgen Mottok, Václav Matoušek
In this paper, we present a novel method for exploiting vulnerabilities in secure embedded bootloaders, which are the foundation of trust for modern vehicle software systems, by using a genetic algorithm to successfully identify the correct parameters to perform an electromagnetic fault injection attack. Specifically, we demonstrate the feasibility of code execution attacks by leveraging a combination of software and hardware weaknesses in the secure software update process of electronic control units (ECUs), which is standardized across the automotive industry. Our method utilizes an automated approach, eliminating the need for static code analysis, and does not require any hardware modifications to the targeted systems. Through our research, we successfully demonstrated our attack on three distinct ECUs from different manufacturers used in current vehicles. Our results prove that the use of a genetic algorithm for finding the fault parameters reduces the number of attempts necessary for a successful fault to obtain arbitrary code execution via "wild jungle jumps" by approximately 100 times compared to a naive random search.
{"title":"An evolutionary fault injection settings search algorithm for attacks on safe and secure embedded systems","authors":"Enrico Pozzobon, Nils Weiß, Jürgen Mottok, Václav Matoušek","doi":"10.14311/nnw.2023.33.020","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.020","url":null,"abstract":"In this paper, we present a novel method for exploiting vulnerabilities in secure embedded bootloaders, which are the foundation of trust for modern vehicle software systems, by using a genetic algorithm to successfully identify the correct parameters to perform an electromagnetic fault injection attack. Specifically, we demonstrate the feasibility of code execution attacks by leveraging a combination of software and hardware weaknesses in the secure software update process of electronic control units (ECUs), which is standardized across the automotive industry. Our method utilizes an automated approach, eliminating the need for static code analysis, and does not require any hardware modifications to the targeted systems. Through our research, we successfully demonstrated our attack on three distinct ECUs from different manufacturers used in current vehicles. Our results prove that the use of a genetic algorithm for finding the fault parameters reduces the number of attempts necessary for a successful fault to obtain arbitrary code execution via \"wild jungle jumps\" by approximately 100 times compared to a naive random search.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135613024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To prevent collisions between trains and objects on the railway line, rugged trains require an intelligent rail protection system. To improve railway safety and reduce the number of accidents, studies are underway. Machine learning (ML) had progressed rapidly, creating new perspectives on the subject. A technique called speed up robust features (SURF) is proposed by researchers to collect regionally and globally relevant information. In addition, taking advantage of the Ohio State University (OSU) heat walker benchmarking dataset, the effectiveness of this technique was examined under various lighting scenarios. This technology could help in reducing train accident rates and financial costs. The findings of the proposed methodology are very specific, in addition to the ability to quickly identify items (obstacles) on the railway line, both of which contribute to rail security. The proposed faster region based convolutional neural network (FR-CNN) with 2D singular spectrum analysis (SSA) improves the performance analysis of an accuracy of 90.2%, recall 95.6% and precision 94.6% when compared with an existing system YOLOv2 and YOLOv5 with 2D SSA.
{"title":"Towards the development of obstacle detection in railway tracks using thermal imaging","authors":"Veeman Vivek, Jeyaprakash Hemalatha, Thamarai Pugazhendhi Latchoumi, Sekar Mohan","doi":"10.14311/nnw.2023.33.019","DOIUrl":"https://doi.org/10.14311/nnw.2023.33.019","url":null,"abstract":"To prevent collisions between trains and objects on the railway line, rugged trains require an intelligent rail protection system. To improve railway safety and reduce the number of accidents, studies are underway. Machine learning (ML) had progressed rapidly, creating new perspectives on the subject. A technique called speed up robust features (SURF) is proposed by researchers to collect regionally and globally relevant information. In addition, taking advantage of the Ohio State University (OSU) heat walker benchmarking dataset, the effectiveness of this technique was examined under various lighting scenarios. This technology could help in reducing train accident rates and financial costs. The findings of the proposed methodology are very specific, in addition to the ability to quickly identify items (obstacles) on the railway line, both of which contribute to rail security. The proposed faster region based convolutional neural network (FR-CNN) with 2D singular spectrum analysis (SSA) improves the performance analysis of an accuracy of 90.2%, recall 95.6% and precision 94.6% when compared with an existing system YOLOv2 and YOLOv5 with 2D SSA.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135613026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.003
K. Daqrouq, A. Alkhateeb, W. Ahmad, Emad Khalaf, Mohamed Awad, E. Noeth, R. Alharbey, A. Rushdi
The electrocardiograph (ECG) is one of the most successful medical diagnostic tools. The ECG can show, roughly speaking, all types of heart disordersthat appear as ECG signal arrhythmias or problems with the rate or rhythm of thehuman heartbeat. In this paper, a universal ECG signal arrhythmia classificationsystem is proposed. The proposed system is based on using the wavelet transformin two of its known forms, namely, the discrete wavelet transform (DWT) andthe wavelet packet transform (WPT), or a combination thereof. The purpose ofthe research reported herein is to find out a universal classification system; in thesense of providing a capability for simultaneous classification of all types of known heart arrhythmias. Three algorithms based on the wavelet transform are tested for different wavelet levels, wavelet functions, training and testing ratios, and elapsed times. We rank these algorithms according to the elapsed times needed for their processing over the whole loop of the eight different arrhythmia classes. This ranking nominates the WPT-based algorithm to be the most superior method among the competing methods. A different ranking according to successful recognition rates assigns priority instead to the method combining the WPT and the DWT.
{"title":"A universal ECG signal classification system using the wavelet transform","authors":"K. Daqrouq, A. Alkhateeb, W. Ahmad, Emad Khalaf, Mohamed Awad, E. Noeth, R. Alharbey, A. Rushdi","doi":"10.14311/nnw.2022.32.003","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.003","url":null,"abstract":"The electrocardiograph (ECG) is one of the most successful medical diagnostic tools. The ECG can show, roughly speaking, all types of heart disordersthat appear as ECG signal arrhythmias or problems with the rate or rhythm of thehuman heartbeat. In this paper, a universal ECG signal arrhythmia classificationsystem is proposed. The proposed system is based on using the wavelet transformin two of its known forms, namely, the discrete wavelet transform (DWT) andthe wavelet packet transform (WPT), or a combination thereof. The purpose ofthe research reported herein is to find out a universal classification system; in thesense of providing a capability for simultaneous classification of all types of known heart arrhythmias. Three algorithms based on the wavelet transform are tested for different wavelet levels, wavelet functions, training and testing ratios, and elapsed times. We rank these algorithms according to the elapsed times needed for their processing over the whole loop of the eight different arrhythmia classes. This ranking nominates the WPT-based algorithm to be the most superior method among the competing methods. A different ranking according to successful recognition rates assigns priority instead to the method combining the WPT and the DWT.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"89 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.014
Ş. Yıldırım, Aslı Durmuşoğlu, Caglar Sevim, Mehmet Safa Bingol, M. Kalkat
Nowadays, some unexpected viruses are affecting people with many troubles. COVID-19 virus is spread in the world very rapidly. However, it seems that predicting cases and death fatalities is not easy. Artificial neural networks are employed in many areas for predicting the systems parameters in simulation or real-time approaches. This paper presents the design of neural predictors for analysing the cases of COVID-19 in three countries. Three countries were selected because of their different regions. Especially, these major countries cases were selected for predicting future effects. Furthermore, three types of neural network predictors were employed to analyse COVID-19 cases. NAR-NN is one of the proposed neural networks that have three layers with one input layer neurons, hidden layer neurons and an output layer with fifteen neurons. Each neuron consisted of the activation functions of the tan-sigmoid. The other proposed neural network, ANFIS, consists of five layers with two inputs and one output and ARIMA uses four iterative steps to predict. The proposed neural network types have been selected from many other types of neural network types. These neural network structures are feed-forward types rather than recurrent neural networks. Learning time is better and faster than other types of networks. Finally, three types of neural predictors were used to predict the cases. The R2 and MSE results improved that three types of neural networks have good performance to predict and analyse three region cases of countries.
{"title":"Design of neural predictors for predicting and analysing COVID-19 cases in different regions","authors":"Ş. Yıldırım, Aslı Durmuşoğlu, Caglar Sevim, Mehmet Safa Bingol, M. Kalkat","doi":"10.14311/nnw.2022.32.014","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.014","url":null,"abstract":"Nowadays, some unexpected viruses are affecting people with many troubles. COVID-19 virus is spread in the world very rapidly. However, it seems that predicting cases and death fatalities is not easy. Artificial neural networks are employed in many areas for predicting the systems parameters in simulation or real-time approaches. This paper presents the design of neural predictors for analysing the cases of COVID-19 in three countries. Three countries were selected because of their different regions. Especially, these major countries cases were selected for predicting future effects. Furthermore, three types of neural network predictors were employed to analyse COVID-19 cases. NAR-NN is one of the proposed neural networks that have three layers with one input layer neurons, hidden layer neurons and an output layer with fifteen neurons. Each neuron consisted of the activation functions of the tan-sigmoid. The other proposed neural network, ANFIS, consists of five layers with two inputs and one output and ARIMA uses four iterative steps to predict. The proposed neural network types have been selected from many other types of neural network types. These neural network structures are feed-forward types rather than recurrent neural networks. Learning time is better and faster than other types of networks. Finally, three types of neural predictors were used to predict the cases. The R2 and MSE results improved that three types of neural networks have good performance to predict and analyse three region cases of countries.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.015
L. Lhotská, Jan Husák, J. Stejskal, M. Kotek, J. Dolezal, Jindrich Adolf
Virtual reality (VR) has been on the scene for several decades already. Its first applications were in gaming. However, hardware and software were expensive and thus not for everybody. Since that time, the development of technology proceeded fast and enabled to open new application areas for VR. Currently many commercial systems are available for gaming, training and education, simulations, design, and also for medical purposes. In the article we focus on VR applications in healthcare. First we present existing commercial solutions, and research studies showing the potential of VR in healthcare. In recent years there have appeared many interesting projects and applications aimed at ageing population as target users. We present examples of such projects. Based on our previous experience and after analysis of available solutions, we propose a conceptual architecture od software environment for development of such applications and discuss their potential use. Finally, the implementation of the proposed architecture for interactive application of experience sets is described.
{"title":"Role of virtual reality in the life of ageing population","authors":"L. Lhotská, Jan Husák, J. Stejskal, M. Kotek, J. Dolezal, Jindrich Adolf","doi":"10.14311/nnw.2022.32.015","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.015","url":null,"abstract":"Virtual reality (VR) has been on the scene for several decades already. Its first applications were in gaming. However, hardware and software were expensive and thus not for everybody. Since that time, the development of technology proceeded fast and enabled to open new application areas for VR. Currently many commercial systems are available for gaming, training and education, simulations, design, and also for medical purposes. In the article we focus on VR applications in healthcare. First we present existing commercial solutions, and research studies showing the potential of VR in healthcare. In recent years there have appeared many interesting projects and applications aimed at ageing population as target users. We present examples of such projects. Based on our previous experience and after analysis of available solutions, we propose a conceptual architecture od software environment for development of such applications and discuss their potential use. Finally, the implementation of the proposed architecture for interactive application of experience sets is described.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.019
K. Sumathi, Viji Vinod
Ripeness estimation of fruits is an essential process that impact the quality of fruits and its marketing. Nearly 30% to 35% get wasted from the harvested fruits due to lack of skilled workers in classification and fruit grading. Although it can be executed by human assessment, it is time consuming, costlier and error prone. Lot of research is carried to automate the quality assessment of fruits. Several hyper-parameters have been considered which have liven up by providing robust convolutional neural network (CNN). This paper has focused on image resizer stochastic gradient descent (SGD) algorithm for computing the loss. It updates the parameter by concentrating channels with respect to red, green, and blue (RGB) to identify and classify the images as ripen and rotten. The real time dataset (6702 images) of oranges, papaya and banana is collected. Using SGD optimizer, learning rate of 0.01 and nearest neighbor interpolation algorithm as resizer, the proposed model has achieved accuracy rate of 96.56% after 38 epochs in classifying the fruits as ripen and rotten. It is also observed that it is possible to use small dataset on visual geometry group with 16 layer (VGG) with the above specification and good accuracy rate can be achieved.
{"title":"Classification of fruits ripeness using CNN with multivariate analysis by SGD","authors":"K. Sumathi, Viji Vinod","doi":"10.14311/nnw.2022.32.019","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.019","url":null,"abstract":"Ripeness estimation of fruits is an essential process that impact the quality of fruits and its marketing. Nearly 30% to 35% get wasted from the harvested fruits due to lack of skilled workers in classification and fruit grading. Although it can be executed by human assessment, it is time consuming, costlier and error prone. Lot of research is carried to automate the quality assessment of fruits. Several hyper-parameters have been considered which have liven up by providing robust convolutional neural network (CNN). This paper has focused on image resizer stochastic gradient descent (SGD) algorithm for computing the loss. It updates the parameter by concentrating channels with respect to red, green, and blue (RGB) to identify and classify the images as ripen and rotten. The real time dataset (6702 images) of oranges, papaya and banana is collected. Using SGD optimizer, learning rate of 0.01 and nearest neighbor interpolation algorithm as resizer, the proposed model has achieved accuracy rate of 96.56% after 38 epochs in classifying the fruits as ripen and rotten. It is also observed that it is possible to use small dataset on visual geometry group with 16 layer (VGG) with the above specification and good accuracy rate can be achieved.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"31 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.005
R. Likhonina, Evženie Uglickich
The present paper describes hand detection application implemented on Xilinx Zynq Ultrascale+ device, comprising multi-core processor ARM Cortex A53 and FPGA programmable logic. It uses ultrasound data and is based on adaptive QRD RLS lattice algorithm extended with hypothesis testing. The algorithm chooses between two use-cases: (1) there is a hand in front of the device vs (2) there is no hand in front of the device. For these purposes a new structure of the identification models was designed. The model presenting use-case (1) is a regression model, which has the order sufficient to cover all incoming data. The model responsible for use-case (2) is a regression model, which has a smaller order than the model (1) and a certain time delay, covering the maximal distance where the hand can possibly appear. The offered concept was successfully verified using real ultrasound data in MATLAB optimized for parallel processing and implemented in parallel on four cores of ARM Cortex A53 processor. It was proved that computational time of the algorithm is sufficient for applications requiring real-time processing.
{"title":"Hand detection application based on QRD RLS lattice algorithm and its implementation on Xilinx Zynq Ultrascale+","authors":"R. Likhonina, Evženie Uglickich","doi":"10.14311/nnw.2022.32.005","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.005","url":null,"abstract":"The present paper describes hand detection application implemented on Xilinx Zynq Ultrascale+ device, comprising multi-core processor ARM Cortex A53 and FPGA programmable logic. It uses ultrasound data and is based on adaptive QRD RLS lattice algorithm extended with hypothesis testing. The algorithm chooses between two use-cases: (1) there is a hand in front of the device vs (2) there is no hand in front of the device. For these purposes a new structure of the identification models was designed. The model presenting use-case (1) is a regression model, which has the order sufficient to cover all incoming data. The model responsible for use-case (2) is a regression model, which has a smaller order than the model (1) and a certain time delay, covering the maximal distance where the hand can possibly appear. The offered concept was successfully verified using real ultrasound data in MATLAB optimized for parallel processing and implemented in parallel on four cores of ARM Cortex A53 processor. It was proved that computational time of the algorithm is sufficient for applications requiring real-time processing.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.008
Qiang Li, Yan Qiang, Demeng Kong, Xiao-feng Liu
The stability assessment of a large power system in real-time is very necessary after it encounters fault. The paper proposes a new model (SVM-GDPSO) for assessing the large power system. In order to enhance SVM, taking tangent vector of power flow Jacobian (PFJ) as the goal of machine learning was used for improving the precision. Besides, particle swarm optimization (PSO) with Gaussian disturbance (GD) is taken for setting the key parameters of SVM, and metalearning was utilized to decrease the search space of PSO. The experiment on the standard test system of IEEE 118-bus demonstrated that this model could reflect the status of large power system in time. Besides, the method could locate the fault area and rank the fault level by the observation of critical bus. The proposed method has the reliability rate 97.22 %, which is superior to the back propagation neural network (BPNN) and SVM-GA, as well as determines the fault area with the success rate of 96.61 %.
{"title":"A model based on SVM-GDPSO for the voltage stability forecasting of large power system","authors":"Qiang Li, Yan Qiang, Demeng Kong, Xiao-feng Liu","doi":"10.14311/nnw.2022.32.008","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.008","url":null,"abstract":"The stability assessment of a large power system in real-time is very necessary after it encounters fault. The paper proposes a new model (SVM-GDPSO) for assessing the large power system. In order to enhance SVM, taking tangent vector of power flow Jacobian (PFJ) as the goal of machine learning was used for improving the precision. Besides, particle swarm optimization (PSO) with Gaussian disturbance (GD) is taken for setting the key parameters of SVM, and metalearning was utilized to decrease the search space of PSO. The experiment on the standard test system of IEEE 118-bus demonstrated that this model could reflect the status of large power system in time. Besides, the method could locate the fault area and rank the fault level by the observation of critical bus. The proposed method has the reliability rate 97.22 %, which is superior to the back propagation neural network (BPNN) and SVM-GA, as well as determines the fault area with the success rate of 96.61 %.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.27.012
S. Bandi, M. Anbarasan, D. Sheela
Sensors of different wavelengths in remote sensing field capture data. Each and every sensor has its own capabilities and limitations. Synthetic aperture radar (SAR) collects data that has a high spatial and radiometric resolution. The optical remote sensors capture images with good spectral information. Fused images from these sensors will have high information when implemented with a better algorithm resulting in the proper collection of data to predict weather forecasting, soil exploration, and crop classification. This work encompasses a fusion of optical and radar data of Sentinel series satellites using a deep learning-based convolutional neural network (CNN). The three-fold work of the image fusion approach is performed in CNN as layered architecture covering the image transform in the convolutional layer, followed by the activity level measurement in the max pooling layer. Finally, the decision-making is performed in the fully connected layer. The objective of the work is to show that the proposed deep learning-based CNN fusion approach overcomes some of the difficulties in the traditional image fusion approaches. To show the performance of the CNN-based image fusion, a good number of image quality assessment metrics are analyzed. The consequences demonstrate that the integration of spatial and spectral information is numerically evident in the output image and has high robustness. Finally, the objective assessment results outperform the state-of-the-art fusion methodologies.
{"title":"Fusion of SAR and optical images using pixel-based CNN","authors":"S. Bandi, M. Anbarasan, D. Sheela","doi":"10.14311/nnw.2022.27.012","DOIUrl":"https://doi.org/10.14311/nnw.2022.27.012","url":null,"abstract":"Sensors of different wavelengths in remote sensing field capture data. Each and every sensor has its own capabilities and limitations. Synthetic aperture radar (SAR) collects data that has a high spatial and radiometric resolution. The optical remote sensors capture images with good spectral information. Fused images from these sensors will have high information when implemented with a better algorithm resulting in the proper collection of data to predict weather forecasting, soil exploration, and crop classification. This work encompasses a fusion of optical and radar data of Sentinel series satellites using a deep learning-based convolutional neural network (CNN). The three-fold work of the image fusion approach is performed in CNN as layered architecture covering the image transform in the convolutional layer, followed by the activity level measurement in the max pooling layer. Finally, the decision-making is performed in the fully connected layer. The objective of the work is to show that the proposed deep learning-based CNN fusion approach overcomes some of the difficulties in the traditional image fusion approaches. To show the performance of the CNN-based image fusion, a good number of image quality assessment metrics are analyzed. The consequences demonstrate that the integration of spatial and spectral information is numerically evident in the output image and has high robustness. Finally, the objective assessment results outperform the state-of-the-art fusion methodologies.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.001
Yesim Yilmaz Abeska, Levent Çavaş
Nanomaterials draw attention because of their unique physical, chemical and biological properties in areas such as catalysis, electronic, optics, medicine, solar energy conversion and water treatment. Green synthesis of silver nanoparticles has many superiorities compared to physical and chemical methods such as lowcost, nontoxicity, eco-sensitive. In this paper, experimental conditions related togreen synthesis of silver nanoparticles by honey were modelled using artificial neural network (ANN). While agitation time, agitation rate, pH, temperature, honey concentration, AgNO3 concentration were selected as input parameters, production of silver nanoparticles was used as an output parameter. According to the results, optimum hidden neuron number was found as 40 with LevenbergMarquardt back-propagation algorithm. In this conditions, the percentages of training, validationand testing were 75, 20 and 5, respectively. After creating neural network separated input data set was applied and then experimental and ANN predicted data were compared. In conclusion, ANN can be an alternative modelling and robust approach that could help researchers in this field to estimate production of silver nanoparticles.
{"title":"Artificial neural network modelling of green synthesis of silver nanoparticles by honey","authors":"Yesim Yilmaz Abeska, Levent Çavaş","doi":"10.14311/nnw.2022.32.001","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.001","url":null,"abstract":"Nanomaterials draw attention because of their unique physical, chemical and biological properties in areas such as catalysis, electronic, optics, medicine, solar energy conversion and water treatment. Green synthesis of silver nanoparticles has many superiorities compared to physical and chemical methods such as lowcost, nontoxicity, eco-sensitive. In this paper, experimental conditions related togreen synthesis of silver nanoparticles by honey were modelled using artificial neural network (ANN). While agitation time, agitation rate, pH, temperature, honey concentration, AgNO3 concentration were selected as input parameters, production of silver nanoparticles was used as an output parameter. According to the results, optimum hidden neuron number was found as 40 with LevenbergMarquardt back-propagation algorithm. In this conditions, the percentages of training, validationand testing were 75, 20 and 5, respectively. After creating neural network separated input data set was applied and then experimental and ANN predicted data were compared. In conclusion, ANN can be an alternative modelling and robust approach that could help researchers in this field to estimate production of silver nanoparticles.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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