Pub Date : 2020-06-27DOI: 10.22044/JADM.2020.8928.2029
Z. Anari, A. Hatamlou, B. Anari, Mohammad Masdari
The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since the membership functions of each web page are different from those of other web pages, so automatic finding the number and position of TMF is significant. In this paper, a different reinforcement-based optimization approach called LA-OMF was proposed to find both the number and positions of TMFs for fuzzy association rules. In the proposed algorithm, the centers and spreads of TMFs were considered as parameters of the search space, and a new representation using learning automata (LA) was proposed to optimize these parameters. The performance of the proposed approach was evaluated and the results were compared with the results of other algorithms on a real dataset. Experiments on datasets with different sizes confirmed that the proposed LA-OMF improved the efficiency of mining fuzzy association rules by extracting optimized membership functions.
{"title":"Optimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining","authors":"Z. Anari, A. Hatamlou, B. Anari, Mohammad Masdari","doi":"10.22044/JADM.2020.8928.2029","DOIUrl":"https://doi.org/10.22044/JADM.2020.8928.2029","url":null,"abstract":"The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since the membership functions of each web page are different from those of other web pages, so automatic finding the number and position of TMF is significant. In this paper, a different reinforcement-based optimization approach called LA-OMF was proposed to find both the number and positions of TMFs for fuzzy association rules. In the proposed algorithm, the centers and spreads of TMFs were considered as parameters of the search space, and a new representation using learning automata (LA) was proposed to optimize these parameters. The performance of the proposed approach was evaluated and the results were compared with the results of other algorithms on a real dataset. Experiments on datasets with different sizes confirmed that the proposed LA-OMF improved the efficiency of mining fuzzy association rules by extracting optimized membership functions.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47464125","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 : 2020-06-23DOI: 10.22044/JADM.2020.8964.2034
A. Tajary, E. Tahanian
Wireless network on chip (WiNoC) is one of the promising on-chip interconnection networks for on-chip system architectures. In addition to wired links, these architectures also use wireless links. Using these wireless links makes packets reach destination nodes faster and with less power consumption. These wireless links are provided by wireless interfaces in wireless routers. The WiNoC architectures differ in the position of the wireless routers and how they interact with other routers. So, the placement of wireless interfaces is an important step in designing WiNoC architectures. In this paper, we propose a simulated annealing (SA) placement method which considers the routing algorithm as a factor in designing cost function. To evaluate the proposed method, the Noxim, which is a cycle-accurate network-on-chip simulator, is used. The simulation results show that the proposed method can reduce flit latency by up to 24.6% with about a 0.2% increase in power consumption.
{"title":"A Routing-Aware Simulated Annealing-based Placement Method in Wireless Network on Chips","authors":"A. Tajary, E. Tahanian","doi":"10.22044/JADM.2020.8964.2034","DOIUrl":"https://doi.org/10.22044/JADM.2020.8964.2034","url":null,"abstract":"Wireless network on chip (WiNoC) is one of the promising on-chip interconnection networks for on-chip system architectures. In addition to wired links, these architectures also use wireless links. Using these wireless links makes packets reach destination nodes faster and with less power consumption. These wireless links are provided by wireless interfaces in wireless routers. The WiNoC architectures differ in the position of the wireless routers and how they interact with other routers. So, the placement of wireless interfaces is an important step in designing WiNoC architectures. In this paper, we propose a simulated annealing (SA) placement method which considers the routing algorithm as a factor in designing cost function. To evaluate the proposed method, the Noxim, which is a cycle-accurate network-on-chip simulator, is used. The simulation results show that the proposed method can reduce flit latency by up to 24.6% with about a 0.2% increase in power consumption.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46375621","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 : 2020-06-23DOI: 10.22044/JADM.2020.7826.1941
V. Ghasemi, M. Javadian, S. Shouraki
In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the data points as one-dimensional ink drop patterns, in order to summarize the effects of all data points, and then applies a threshold on the resulting vectors. It is based on an ensemble clustering method which performs one-dimensional density partitioning to produce ensemble of clustering solutions. Then, it assigns a unique prime number to the data points that exist in each partition as their labels. Consequently, a combination is performed by multiplying the labels of every data point in order to produce the absolute labels. The data points with identical absolute labels are fallen into the same cluster. The hierarchical property of the algorithm is intended to cluster complex data by zooming in each already formed cluster to find further sub-clusters. The algorithm is verified using several synthetic and real-world datasets. The results show that the proposed method has a promising performance, compared to some well-known high-dimensional data clustering algorithms.
{"title":"High-Dimensional Unsupervised Active Learning Method","authors":"V. Ghasemi, M. Javadian, S. Shouraki","doi":"10.22044/JADM.2020.7826.1941","DOIUrl":"https://doi.org/10.22044/JADM.2020.7826.1941","url":null,"abstract":"In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the data points as one-dimensional ink drop patterns, in order to summarize the effects of all data points, and then applies a threshold on the resulting vectors. It is based on an ensemble clustering method which performs one-dimensional density partitioning to produce ensemble of clustering solutions. Then, it assigns a unique prime number to the data points that exist in each partition as their labels. Consequently, a combination is performed by multiplying the labels of every data point in order to produce the absolute labels. The data points with identical absolute labels are fallen into the same cluster. The hierarchical property of the algorithm is intended to cluster complex data by zooming in each already formed cluster to find further sub-clusters. The algorithm is verified using several synthetic and real-world datasets. The results show that the proposed method has a promising performance, compared to some well-known high-dimensional data clustering algorithms.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49325356","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 : 2020-06-23DOI: 10.22044/JADM.2020.9025.2040
H. Rahmani, H. Kamali, H. Shah-Hosseini
Nowadays, a significant amount of studies are devoted to discovering important nodes in graph data. Social networks as graph data have attracted a lot of attention. There are various purposes for discovering the important nodes in social networks such as finding the leaders in them, i.e. the users who play an important role in promoting advertising, etc. Different criteria have been proposed in discovering important nodes in graph data. Measuring a node’s importance by a single criterion may be inefficient due to the variety of graph structures. Recently, a combination of criteria has been used in the discovery of important nodes. In this paper, we propose a system for the Discovery of Important Nodes in social networks using Genetic Algorithms (DINGA). In our proposed system, important nodes in social networks are discovered by employing a combination of eight informative criteria and their intelligent weighting. We compare our results with a manually weighted method, that uses random weightings for each criterion, in four real networks. Our method shows an average of 22% improvement in the accuracy of important nodes discovery.
{"title":"DINGA: A Genetic-algorithm-based Method for Finding Important Nodes in Social Networks","authors":"H. Rahmani, H. Kamali, H. Shah-Hosseini","doi":"10.22044/JADM.2020.9025.2040","DOIUrl":"https://doi.org/10.22044/JADM.2020.9025.2040","url":null,"abstract":"Nowadays, a significant amount of studies are devoted to discovering important nodes in graph data. Social networks as graph data have attracted a lot of attention. There are various purposes for discovering the important nodes in social networks such as finding the leaders in them, i.e. the users who play an important role in promoting advertising, etc. Different criteria have been proposed in discovering important nodes in graph data. Measuring a node’s importance by a single criterion may be inefficient due to the variety of graph structures. Recently, a combination of criteria has been used in the discovery of important nodes. In this paper, we propose a system for the Discovery of Important Nodes in social networks using Genetic Algorithms (DINGA). In our proposed system, important nodes in social networks are discovered by employing a combination of eight informative criteria and their intelligent weighting. We compare our results with a manually weighted method, that uses random weightings for each criterion, in four real networks. Our method shows an average of 22% improvement in the accuracy of important nodes discovery.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44284770","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 : 2020-06-14DOI: 10.22044/JADM.2020.8865.2021
Gh. Ahmadi, M. Teshnelab
Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different purposes such as prediction, fault detection, and control. In the previous works, CRK was identified after decomposing it into several multiple input-single output (MISO) systems. In this paper, for the first time, the rough-neural network (R-NN) is utilized for the identification of CRK without the usage of MISO structures. R-NN is a neural structure designed on the base of rough set theory for dealing with the uncertainty and vagueness. In addition, a stochastic gradient descent learning algorithm is proposed for training the R-NNs. The simulation results show the effectiveness of proposed methodology.
{"title":"Identification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network","authors":"Gh. Ahmadi, M. Teshnelab","doi":"10.22044/JADM.2020.8865.2021","DOIUrl":"https://doi.org/10.22044/JADM.2020.8865.2021","url":null,"abstract":"Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different purposes such as prediction, fault detection, and control. In the previous works, CRK was identified after decomposing it into several multiple input-single output (MISO) systems. In this paper, for the first time, the rough-neural network (R-NN) is utilized for the identification of CRK without the usage of MISO structures. R-NN is a neural structure designed on the base of rough set theory for dealing with the uncertainty and vagueness. In addition, a stochastic gradient descent learning algorithm is proposed for training the R-NNs. The simulation results show the effectiveness of proposed methodology.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45348184","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 : 2020-06-09DOI: 10.22044/JADM.2020.8438.1982
Hossein Gholamalinezhad, H. Khosravi
In recent years, vehicle classification has been one of the most important research topics. However, due to the lack of a proper dataset, this field has not been well developed as other fields of intelligent traffic management. Therefore, the preparation of large-scale datasets of vehicles for each country is of great interest. In this paper, we introduce a new standard dataset of popular Iranian vehicles. This dataset, which consists of images from moving vehicles in urban streets and highways, can be used for vehicle classification and license plate recognition. It contains a large collection of vehicle images in different dimensions, viewing angles, weather, and lighting conditions. It took more than a year to construct this dataset. Images are taken from various types of mounted cameras, with different resolutions and at different altitudes. To estimate the complexity of the dataset, some classic methods alongside popular Deep Neural Networks are trained and evaluated on the dataset. Furthermore, two light-weight CNN structures are also proposed. One with 3-Conv layers and another with 5-Conv layers. The 5-Conv model with 152K parameters reached the recognition rate of 99.09% and can process 48 frames per second on CPU which is suitable for real-time applications.
{"title":"IRVD: A Large-Scale Dataset for Classification of Iranian Vehicles in Urban Streets","authors":"Hossein Gholamalinezhad, H. Khosravi","doi":"10.22044/JADM.2020.8438.1982","DOIUrl":"https://doi.org/10.22044/JADM.2020.8438.1982","url":null,"abstract":"In recent years, vehicle classification has been one of the most important research topics. However, due to the lack of a proper dataset, this field has not been well developed as other fields of intelligent traffic management. Therefore, the preparation of large-scale datasets of vehicles for each country is of great interest. In this paper, we introduce a new standard dataset of popular Iranian vehicles. This dataset, which consists of images from moving vehicles in urban streets and highways, can be used for vehicle classification and license plate recognition. It contains a large collection of vehicle images in different dimensions, viewing angles, weather, and lighting conditions. It took more than a year to construct this dataset. Images are taken from various types of mounted cameras, with different resolutions and at different altitudes. To estimate the complexity of the dataset, some classic methods alongside popular Deep Neural Networks are trained and evaluated on the dataset. Furthermore, two light-weight CNN structures are also proposed. One with 3-Conv layers and another with 5-Conv layers. The 5-Conv model with 152K parameters reached the recognition rate of 99.09% and can process 48 frames per second on CPU which is suitable for real-time applications.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46337610","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 : 2020-04-04DOI: 10.22044/JADM.2020.7858.1923
M. Zeynali, Hadi Seyedarabi, B. M. Tazehkand
Network security is very important when sending confidential data through the network. Cryptography is the science of hiding information, and a combination of cryptography solutions with cognitive science starts a new branch called cognitive cryptography that guarantee the confidentiality and integrity of the data. Brain signals as a biometric indicator can convert to a binary code which can be used as a cryptographic key. This paper proposes a new method for decreasing the error of EEG- based key generation process. Discrete Fourier Transform, Discrete Wavelet Transform, Autoregressive Modeling, Energy Entropy, and Sample Entropy were used to extract features. All features are used as the input of new method based on window segmentation protocol then are converted to the binary mode. We obtain 0.76%, and 0.48% mean Half Total Error Rate (HTER) for 18-channel and single-channel cryptographic key generation systems respectively.
{"title":"Development of a Unique Biometric-based Cryptographic Key Generation with Repeatability using Brain Signals","authors":"M. Zeynali, Hadi Seyedarabi, B. M. Tazehkand","doi":"10.22044/JADM.2020.7858.1923","DOIUrl":"https://doi.org/10.22044/JADM.2020.7858.1923","url":null,"abstract":"Network security is very important when sending confidential data through the network. Cryptography is the science of hiding information, and a combination of cryptography solutions with cognitive science starts a new branch called cognitive cryptography that guarantee the confidentiality and integrity of the data. Brain signals as a biometric indicator can convert to a binary code which can be used as a cryptographic key. This paper proposes a new method for decreasing the error of EEG- based key generation process. Discrete Fourier Transform, Discrete Wavelet Transform, Autoregressive Modeling, Energy Entropy, and Sample Entropy were used to extract features. All features are used as the input of new method based on window segmentation protocol then are converted to the binary mode. We obtain 0.76%, and 0.48% mean Half Total Error Rate (HTER) for 18-channel and single-channel cryptographic key generation systems respectively.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47215463","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 : 2020-04-01DOI: 10.22044/JADM.2019.7903.1929
M. Kurmanji, F. Ghaderi
Despite considerable enhances in recognizing hand gestures from still images, there are still many challenges in the classification of hand gestures in videos. The latter comes with more challenges, including higher computational complexity and arduous task of representing temporal features. Hand movement dynamics, represented by temporal features, have to be extracted by analyzing the total frames of a video. So far, both 2D and 3D convolutional neural networks have been used to manipulate the temporal dynamics of the video frames. 3D CNNs can extract the changes in the consecutive frames and tend to be more suitable for the video classification task, however, they usually need more time. On the other hand, by using techniques like tiling it is possible to aggregate all the frames in a single matrix and preserve the temporal and spatial features. This way, using 2D CNNs, which are inherently simpler than 3D CNNs can be used to classify the video instances. In this paper, we compared the application of 2D and 3D CNNs for representing temporal features and classifying hand gesture sequences. Additionally, providing a two-stage two-stream architecture, we efficiently combined color and depth modalities and 2D and 3D CNN predictions. The effect of different types of augmentation techniques is also investigated. Our results confirm that appropriate usage of 2D CNNs outperforms a 3D CNN implementation in this task.
{"title":"Hand Gesture Recognition from RGB-D Data using 2D and 3D Convolutional Neural Networks: a comparative study","authors":"M. Kurmanji, F. Ghaderi","doi":"10.22044/JADM.2019.7903.1929","DOIUrl":"https://doi.org/10.22044/JADM.2019.7903.1929","url":null,"abstract":"Despite considerable enhances in recognizing hand gestures from still images, there are still many challenges in the classification of hand gestures in videos. The latter comes with more challenges, including higher computational complexity and arduous task of representing temporal features. Hand movement dynamics, represented by temporal features, have to be extracted by analyzing the total frames of a video. So far, both 2D and 3D convolutional neural networks have been used to manipulate the temporal dynamics of the video frames. 3D CNNs can extract the changes in the consecutive frames and tend to be more suitable for the video classification task, however, they usually need more time. On the other hand, by using techniques like tiling it is possible to aggregate all the frames in a single matrix and preserve the temporal and spatial features. This way, using 2D CNNs, which are inherently simpler than 3D CNNs can be used to classify the video instances. In this paper, we compared the application of 2D and 3D CNNs for representing temporal features and classifying hand gesture sequences. Additionally, providing a two-stage two-stream architecture, we efficiently combined color and depth modalities and 2D and 3D CNN predictions. The effect of different types of augmentation techniques is also investigated. Our results confirm that appropriate usage of 2D CNNs outperforms a 3D CNN implementation in this task.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47367880","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 : 2020-04-01DOI: 10.22044/JADM.2019.7402.1877
S. Mavaddati
In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts including sparse representation and dictionary learning techniques to yield over-complete models in this processing field. There are color-based, statistical-based and texture-based features to represent the structural content of rice varieties. To achieve the desired results, different features from recorded images are extracted and used to learn the representative models of rice samples. Also, sparse principal component analysis and sparse structured principal component analysis is employed to reduce the dimension of classification problem and lead to an accurate detector with less computational time. The results of the proposed classifier based on the learned models are compared with the results obtained from neural network and support vector machine. Simulation results, along with a meaningful statistical test, show that the proposed algorithm based on the learned dictionaries derived from the combinational features can detect the type of rice grain and determine its quality precisely.
{"title":"Sparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains","authors":"S. Mavaddati","doi":"10.22044/JADM.2019.7402.1877","DOIUrl":"https://doi.org/10.22044/JADM.2019.7402.1877","url":null,"abstract":"In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts including sparse representation and dictionary learning techniques to yield over-complete models in this processing field. There are color-based, statistical-based and texture-based features to represent the structural content of rice varieties. To achieve the desired results, different features from recorded images are extracted and used to learn the representative models of rice samples. Also, sparse principal component analysis and sparse structured principal component analysis is employed to reduce the dimension of classification problem and lead to an accurate detector with less computational time. The results of the proposed classifier based on the learned models are compared with the results obtained from neural network and support vector machine. Simulation results, along with a meaningful statistical test, show that the proposed algorithm based on the learned dictionaries derived from the combinational features can detect the type of rice grain and determine its quality precisely.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43576439","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 : 2020-04-01DOI: 10.22044/JADM.2019.7742.1914
Hamid Haghshenas Gorgani, A. J. Pak
Identification of the factors affecting teaching quality of engineering drawing and interaction between them is necessary until it is determined which manipulation will improve the quality of teaching this course. Since the above issue is a Multi-Criteria Decision Making (MCDM) problem and on the other hand, we are faced with human factors, the Fuzzy DEMATEL method is suggested for solving it. Also, because DEMATEL analysis does not lead to a weighting of the criteria, it is combined with the ANP and a hybrid fuzzy DEMATEL-ANP (FDANP) methodology is used. The results of investigating 7 Dimensions and 21 Criteria show that the quality of teaching this course increases, if the updated teaching methods and contents to be used, the evaluation policy to be tailored to the course, the course professor and his/her assistants be available to correct students' mistakes and there is also an interactive system based on student comments.
{"title":"Identification of Factors Affecting Quality of Teaching Engineering Drawing using a Hybrid MCDM Model","authors":"Hamid Haghshenas Gorgani, A. J. Pak","doi":"10.22044/JADM.2019.7742.1914","DOIUrl":"https://doi.org/10.22044/JADM.2019.7742.1914","url":null,"abstract":"Identification of the factors affecting teaching quality of engineering drawing and interaction between them is necessary until it is determined which manipulation will improve the quality of teaching this course. Since the above issue is a Multi-Criteria Decision Making (MCDM) problem and on the other hand, we are faced with human factors, the Fuzzy DEMATEL method is suggested for solving it. Also, because DEMATEL analysis does not lead to a weighting of the criteria, it is combined with the ANP and a hybrid fuzzy DEMATEL-ANP (FDANP) methodology is used. The results of investigating 7 Dimensions and 21 Criteria show that the quality of teaching this course increases, if the updated teaching methods and contents to be used, the evaluation policy to be tailored to the course, the course professor and his/her assistants be available to correct students' mistakes and there is also an interactive system based on student comments.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49405271","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}
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