Pub Date : 2021-01-01DOI: 10.22044/JADM.2020.10085.2147
Zarbazoo Siahkali, A. Ghaderi, Abdolhamid Bahrpeyma, M. Rashki, N. S. Hamzehkolaei
Scouring, occurring when the water flow erodes the bed materials around the bridge pier structure, is a serious safety assessment problem for which there are many equations and models in the literature to estimate the approximate scour depth. This research is aimed to study how surrogate models estimate the scour depth around circular piers and compare the results with those of the empirical formulations. To this end, the pier scour depth was estimated in non-cohesive soils based on a subcritical flow and live bed conditions using the artificial neural networks (ANN), group method of data handling (GMDH), multivariate adaptive regression splines (MARS) and Gaussian process models (Kriging). A database containing 246 lab data gathered from various studies was formed and the data were divided into three random parts: 1) training, 2) validation and 3) testing to build the surrogate models. The statistical error criteria such as the coefficient of determination (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE) and absolute maximum percentage error (MPE) of the surrogate models were then found and compared with those of the popular empirical formulations. Results revealed that the surrogate models’ test data estimations were more accurate than those of the empirical equations; Kriging has had better estimations than other models. In addition, sensitivity analyses of all surrogate models showed that the pier width’s dimensionless expression (b/y) had a greater effect on estimating the normalized scour depth (Ds/y).
{"title":"Estimating Pier Scour Depth: Comparison of Empirical Formulations with ANNs, GMDH, MARS, and Kriging","authors":"Zarbazoo Siahkali, A. Ghaderi, Abdolhamid Bahrpeyma, M. Rashki, N. S. Hamzehkolaei","doi":"10.22044/JADM.2020.10085.2147","DOIUrl":"https://doi.org/10.22044/JADM.2020.10085.2147","url":null,"abstract":"Scouring, occurring when the water flow erodes the bed materials around the bridge pier structure, is a serious safety assessment problem for which there are many equations and models in the literature to estimate the approximate scour depth. This research is aimed to study how surrogate models estimate the scour depth around circular piers and compare the results with those of the empirical formulations. To this end, the pier scour depth was estimated in non-cohesive soils based on a subcritical flow and live bed conditions using the artificial neural networks (ANN), group method of data handling (GMDH), multivariate adaptive regression splines (MARS) and Gaussian process models (Kriging). A database containing 246 lab data gathered from various studies was formed and the data were divided into three random parts: 1) training, 2) validation and 3) testing to build the surrogate models. The statistical error criteria such as the coefficient of determination (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE) and absolute maximum percentage error (MPE) of the surrogate models were then found and compared with those of the popular empirical formulations. Results revealed that the surrogate models’ test data estimations were more accurate than those of the empirical equations; Kriging has had better estimations than other models. In addition, sensitivity analyses of all surrogate models showed that the pier width’s dimensionless expression (b/y) had a greater effect on estimating the normalized scour depth (Ds/y).","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68374934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.22044/JADM.2020.9376.2072
S. A. Amiri, Muhammad Rajabinasab
Face recognition is a challenging problem because of different illuminations, poses, facial expressions, and occlusions. In this paper, a new robust face recognition method is proposed based on color and edge orientation difference histogram. Firstly, color and edge orientation difference histogram is extracted using color, color difference, edge orientation and edge orientation difference of the face image. Then, backward feature selection is employed to reduce the number of features. Finally, Canberra measure is used to assess the similarity between the images. Color and edge orientation difference histogram shows uniform color difference and edge orientation difference between two neighboring pixels. This histogram will be effective for face recognition due to different skin colors and different edge orientations of the face image, which leads to different light reflection. The proposed method is evaluated on Yale and ORL face datasets. These datasets are consisted of gray-scale face images under different illuminations, poses, facial expressions and occlusions. The recognition rate over Yale and ORL datasets is achieved 100% and 98.75% respectively. Experimental results demonstrate that the proposed method outperforms the existing methods in face recognition.
{"title":"Face Recognition using Color and Edge Orientation Difference Histogram","authors":"S. A. Amiri, Muhammad Rajabinasab","doi":"10.22044/JADM.2020.9376.2072","DOIUrl":"https://doi.org/10.22044/JADM.2020.9376.2072","url":null,"abstract":"Face recognition is a challenging problem because of different illuminations, poses, facial expressions, and occlusions. In this paper, a new robust face recognition method is proposed based on color and edge orientation difference histogram. Firstly, color and edge orientation difference histogram is extracted using color, color difference, edge orientation and edge orientation difference of the face image. Then, backward feature selection is employed to reduce the number of features. Finally, Canberra measure is used to assess the similarity between the images. Color and edge orientation difference histogram shows uniform color difference and edge orientation difference between two neighboring pixels. This histogram will be effective for face recognition due to different skin colors and different edge orientations of the face image, which leads to different light reflection. The proposed method is evaluated on Yale and ORL face datasets. These datasets are consisted of gray-scale face images under different illuminations, poses, facial expressions and occlusions. The recognition rate over Yale and ORL datasets is achieved 100% and 98.75% respectively. Experimental results demonstrate that the proposed method outperforms the existing methods in face recognition.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68374783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.22044/JADM.2020.9309.2068
T. A. Javaran, A. Alidadi, S. R. Arab
Estimation of blurriness value in image is an important issue in image processing applications such as image deblurring. In this paper, a no-reference blur metric with low computational cost is proposed, which is based on the difference between the second order gradients of a sharp image and the one associated with its blurred version. The experiments, in this paper, performed on four databases, including CSIQ, TID2008, IVC, and LIVE. The experimental results indicate the capability of the proposed blur metric in measuring image blurriness, also the low computational cost, comparing with other existing approaches.
{"title":"A No-Reference Blur Metric based on Second-Order Gradients of Image","authors":"T. A. Javaran, A. Alidadi, S. R. Arab","doi":"10.22044/JADM.2020.9309.2068","DOIUrl":"https://doi.org/10.22044/JADM.2020.9309.2068","url":null,"abstract":"Estimation of blurriness value in image is an important issue in image processing applications such as image deblurring. In this paper, a no-reference blur metric with low computational cost is proposed, which is based on the difference between the second order gradients of a sharp image and the one associated with its blurred version. The experiments, in this paper, performed on four databases, including CSIQ, TID2008, IVC, and LIVE. The experimental results indicate the capability of the proposed blur metric in measuring image blurriness, also the low computational cost, comparing with other existing approaches.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68375103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.22044/JADM.2020.9087.2045
S. Javadi, R. Safa, M. Azizi, Seyed Abolghasem Mirroshandel
Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. Online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. In this paper, using a dataset including bibliographic information of user’s publication, the publication venues, and other published papers provided as a way to find an expert in a particular context where experts are recommended to a user according to his records and preferences. In this way, a user request to find an expert is presented with keywords that represent a certain expertise and the system output will be a certain number of ranked suggestions for a specific user. Each suggestion is the name of an expert who has been identified appropriate to collaborate with the user. In evaluation using IEEE database, the proposed method reached an accuracy of 71.50 percent that seems to be an acceptable result.
{"title":"A Recommendation System for Finding Experts in Online Scientific Communities","authors":"S. Javadi, R. Safa, M. Azizi, Seyed Abolghasem Mirroshandel","doi":"10.22044/JADM.2020.9087.2045","DOIUrl":"https://doi.org/10.22044/JADM.2020.9087.2045","url":null,"abstract":"Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. Online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. In this paper, using a dataset including bibliographic information of user’s publication, the publication venues, and other published papers provided as a way to find an expert in a particular context where experts are recommended to a user according to his records and preferences. In this way, a user request to find an expert is presented with keywords that represent a certain expertise and the system output will be a certain number of ranked suggestions for a specific user. Each suggestion is the name of an expert who has been identified appropriate to collaborate with the user. In evaluation using IEEE database, the proposed method reached an accuracy of 71.50 percent that seems to be an acceptable result.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43144118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.22044/JADM.2020.9131.2052
N. Majidi, K. Kiani, R. Rastgoo
This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks simultaneously. We use the residual layers in our model to make repetitive layers, increase the depth of the model, and make an end-to-end model. Furthermore, we employed a deep network in up-sampling step instead of bicubic interpolation method used in most of the previous works. Since the image resolution plays an important role to obtain rich information from the medical images and helps for accurate and faster diagnosis of the ailment, we use the medical images for resolution enhancement. Our model is capable of reconstructing a high-resolution image from low-resolution one in both medical and general images. Evaluation results on TSA and TZDE datasets, including MRI images, and Set5, Set14, B100, and Urban100 datasets, including general images, demonstrate that our model outperforms state-of-the-art alternatives in both areas of medical and general super-resolution enhancement from a single input image.
{"title":"A Deep Model for Super-resolution Enhancement from a Single Image","authors":"N. Majidi, K. Kiani, R. Rastgoo","doi":"10.22044/JADM.2020.9131.2052","DOIUrl":"https://doi.org/10.22044/JADM.2020.9131.2052","url":null,"abstract":"This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks simultaneously. We use the residual layers in our model to make repetitive layers, increase the depth of the model, and make an end-to-end model. Furthermore, we employed a deep network in up-sampling step instead of bicubic interpolation method used in most of the previous works. Since the image resolution plays an important role to obtain rich information from the medical images and helps for accurate and faster diagnosis of the ailment, we use the medical images for resolution enhancement. Our model is capable of reconstructing a high-resolution image from low-resolution one in both medical and general images. Evaluation results on TSA and TZDE datasets, including MRI images, and Set5, Set14, B100, and Urban100 datasets, including general images, demonstrate that our model outperforms state-of-the-art alternatives in both areas of medical and general super-resolution enhancement from a single input image.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42510323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.22044/JADM.2020.8853.2019
R. Mohammadian, M. Mahlouji, A. Shahidinejad
Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. Firstly, the effect of changing the Gabor filter parameters (orientation, frequency, standard deviation, aspect ratio and phase offset) for an image is analysed, secondly, the range of Gabor filter parameter values is determined and finally, the best values for these parameters are specified. A multilayer feedforward neural network with a back-propagation algorithm is used as a classifier. The input vector is obtained by convolving the input image and a Gabor filter, with both the angle and frequency values equal to π/2. The proposed algorithm is tested on 1,484 image samples with simple and complex backgrounds. The experimental results show that the proposed detector achieves great detection accuracy, by comparing it with several popular face-detection algorithms, such as OpenCV’s Viola-Jones detector.
{"title":"Multi-View Face Detection in Open Environments using Gabor Features and Neural Networks","authors":"R. Mohammadian, M. Mahlouji, A. Shahidinejad","doi":"10.22044/JADM.2020.8853.2019","DOIUrl":"https://doi.org/10.22044/JADM.2020.8853.2019","url":null,"abstract":"Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. Firstly, the effect of changing the Gabor filter parameters (orientation, frequency, standard deviation, aspect ratio and phase offset) for an image is analysed, secondly, the range of Gabor filter parameter values is determined and finally, the best values for these parameters are specified. A multilayer feedforward neural network with a back-propagation algorithm is used as a classifier. The input vector is obtained by convolving the input image and a Gabor filter, with both the angle and frequency values equal to π/2. The proposed algorithm is tested on 1,484 image samples with simple and complex backgrounds. The experimental results show that the proposed detector achieves great detection accuracy, by comparing it with several popular face-detection algorithms, such as OpenCV’s Viola-Jones detector.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48138432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.22044/JADM.2020.9193.2055
R. Azizi, A. Latif
In this work, we show that an image reconstruction from a burst of individually demosaicked RAW captures propagates demosaicking artifacts throughout the image processing pipeline. Hence, we propose a joint regularization scheme for burst denoising and demosaicking. We model the burst alignment functions and the color filter array sampling functions into one linear operator. Then, we formulate the individual burst reconstruction and the demosaicking problems into a three-color-channel optimization problem. We introduce a crosschannel prior to the solution of this optimization problem and develop a numerical solver via alternating direction method of multipliers. Moreover, our proposed method avoids the complexity of alignment estimation as a preprocessing step for burst reconstruction. It relies on a phase correlation approach in the Fourier’s domain to efficiently find the relative translation, rotation, and scale among the burst captures and to perform warping accordingly. As a result of these steps, the proposed joint burst denoising and demosaicking solution improves the quality of reconstructed images by a considerable margin compared to existing image model-based methods.
{"title":"Joint Burst Denoising and Demosaicking via Regularization and an Efficient Alignment","authors":"R. Azizi, A. Latif","doi":"10.22044/JADM.2020.9193.2055","DOIUrl":"https://doi.org/10.22044/JADM.2020.9193.2055","url":null,"abstract":"In this work, we show that an image reconstruction from a burst of individually demosaicked RAW captures propagates demosaicking artifacts throughout the image processing pipeline. Hence, we propose a joint regularization scheme for burst denoising and demosaicking. We model the burst alignment functions and the color filter array sampling functions into one linear operator. Then, we formulate the individual burst reconstruction and the demosaicking problems into a three-color-channel optimization problem. We introduce a crosschannel prior to the solution of this optimization problem and develop a numerical solver via alternating direction method of multipliers. Moreover, our proposed method avoids the complexity of alignment estimation as a preprocessing step for burst reconstruction. It relies on a phase correlation approach in the Fourier’s domain to efficiently find the relative translation, rotation, and scale among the burst captures and to perform warping accordingly. As a result of these steps, the proposed joint burst denoising and demosaicking solution improves the quality of reconstructed images by a considerable margin compared to existing image model-based methods.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43402895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.22044/JADM.2020.7444.1886
I. Pasandideh, A. Rajabi, F. Yosefvand, S. Shabanlou
Generally, length of hydraulic jump is one the most important parameters to design stilling basin. In this study, the length of hydraulic jump on sloping rough beds was predicted using Gene Expression Programming (GEP) for the first time. The Monte Carlo simulations were used to examine the ability of the GEP model. In addition, k-fold cross validation was employed in order to verify the results of the GEP model. To determine the length of hydraulic jump, five different GEP models were introduced using input parameters. Then by analyzing the GEP models results, the superior model was presented. For the superior model, correlation coefficient (R), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were computed 0.901, 11.517 and 1.664, respectively. According to the sensitivity analysis, the Froude number at upstream of hydraulic jump was identified as the most important parameter to model the length of hydraulic jump. Furthermore, the partial derivative sensitivity analysis (PDSA) was performed. For instance, the PDSA was calculated as positive for all input variables.
{"title":"Modeling Length of Hydraulic Jump on Sloping Rough Bed using Gene Expression Programming","authors":"I. Pasandideh, A. Rajabi, F. Yosefvand, S. Shabanlou","doi":"10.22044/JADM.2020.7444.1886","DOIUrl":"https://doi.org/10.22044/JADM.2020.7444.1886","url":null,"abstract":"Generally, length of hydraulic jump is one the most important parameters to design stilling basin. In this study, the length of hydraulic jump on sloping rough beds was predicted using Gene Expression Programming (GEP) for the first time. The Monte Carlo simulations were used to examine the ability of the GEP model. In addition, k-fold cross validation was employed in order to verify the results of the GEP model. To determine the length of hydraulic jump, five different GEP models were introduced using input parameters. Then by analyzing the GEP models results, the superior model was presented. For the superior model, correlation coefficient (R), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were computed 0.901, 11.517 and 1.664, respectively. According to the sensitivity analysis, the Froude number at upstream of hydraulic jump was identified as the most important parameter to model the length of hydraulic jump. Furthermore, the partial derivative sensitivity analysis (PDSA) was performed. For instance, the PDSA was calculated as positive for all input variables.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49297478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-12DOI: 10.22044/JADM.2020.9253.2060
M. Y. Tabari, Z. Mataji
The Internet of Things (IoT) is a novel paradigm in computer networks which is capable to connect things to the internet via a wide range of technologies. Due to the features of the sensors used in IoT networks and the unsecured nature of the internet, IoT is vulnerable to many internal routing attacks. Using traditional IDS in these networks has its own challenges due to the resource constraint of the nodes, and the characteristics of the IoT network. A sinkhole attacker node, in this network, attempts to attract traffic through incorrect information advertisement. In this research, a distributed IDS architecture is proposed to detect sinkhole routing attack in RPL-based IoT networks, which is aimed to improve true detection rate and reduce the false alarms. For the latter we used one type of post processing mechanism in which a threshold is defined for separating suspicious alarms for further verifications. Also, the implemented IDS modules distributed via client and router border nodes that makes it energy efficient. The required data for interpretation of network’s behavior gathered from scenarios implemented in Cooja environment with the aim of Rapidminer for mining the produces patterns. The produced dataset optimized using Genetic algorithm by selecting appropriate features. We investigate three different classification algorithms which in its best case Decision Tree could reaches to 99.35 rate of accuracy.
{"title":"Detecting Sinkhole Attack in RPL-based Internet of Things Routing Protocol","authors":"M. Y. Tabari, Z. Mataji","doi":"10.22044/JADM.2020.9253.2060","DOIUrl":"https://doi.org/10.22044/JADM.2020.9253.2060","url":null,"abstract":"The Internet of Things (IoT) is a novel paradigm in computer networks which is capable to connect things to the internet via a wide range of technologies. Due to the features of the sensors used in IoT networks and the unsecured nature of the internet, IoT is vulnerable to many internal routing attacks. Using traditional IDS in these networks has its own challenges due to the resource constraint of the nodes, and the characteristics of the IoT network. A sinkhole attacker node, in this network, attempts to attract traffic through incorrect information advertisement. In this research, a distributed IDS architecture is proposed to detect sinkhole routing attack in RPL-based IoT networks, which is aimed to improve true detection rate and reduce the false alarms. For the latter we used one type of post processing mechanism in which a threshold is defined for separating suspicious alarms for further verifications. Also, the implemented IDS modules distributed via client and router border nodes that makes it energy efficient. The required data for interpretation of network’s behavior gathered from scenarios implemented in Cooja environment with the aim of Rapidminer for mining the produces patterns. The produced dataset optimized using Genetic algorithm by selecting appropriate features. We investigate three different classification algorithms which in its best case Decision Tree could reaches to 99.35 rate of accuracy.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43572934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-12DOI: 10.22044/JADM.2020.9579.2091
A. Lakizadeh, Z. Zinaty
Aspect-level sentiment classification is an essential issue in sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. Recent methods have discovered the role of aspects in sentiment polarity classification and developed various techniques to assess the sentiment polarity of each aspect in the text. However, these studies do not pay enough attention to the need for vectors to be optimal for the aspect. To address this issue, in the present study, we suggest a Hierarchical Attention-based Method (HAM) for aspect-based polarity classification of the text. HAM works in a hierarchically manner; firstly, it extracts an embedding vector for aspects. Next, it employs these aspect vectors with information content to determine the sentiment of the text. The experimental findings on the SemEval2014 data set show that HAM can improve accuracy by up to 6.74% compared to the state-of-the-art methods in aspect-based sentiment classification task.
{"title":"A Novel Hierarchical Attention-based Method for Aspect-level Sentiment Classification","authors":"A. Lakizadeh, Z. Zinaty","doi":"10.22044/JADM.2020.9579.2091","DOIUrl":"https://doi.org/10.22044/JADM.2020.9579.2091","url":null,"abstract":"Aspect-level sentiment classification is an essential issue in sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. Recent methods have discovered the role of aspects in sentiment polarity classification and developed various techniques to assess the sentiment polarity of each aspect in the text. However, these studies do not pay enough attention to the need for vectors to be optimal for the aspect. To address this issue, in the present study, we suggest a Hierarchical Attention-based Method (HAM) for aspect-based polarity classification of the text. HAM works in a hierarchically manner; firstly, it extracts an embedding vector for aspects. Next, it employs these aspect vectors with information content to determine the sentiment of the text. The experimental findings on the SemEval2014 data set show that HAM can improve accuracy by up to 6.74% compared to the state-of-the-art methods in aspect-based sentiment classification task.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49407842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：