Pub Date : 2018-09-01DOI: 10.23919/IConAC.2018.8749115
Huiming Yao, C. Ulianov, Feng Liu
The paper proposes a new feature pattern recognition method for early warning of defects of the railway vehicle running gear. Based on a large amount of historical data, a joint self-learning and fuzzy clustering algorithm was developed. The joint algorithm combines the advantages of the fuzzy clustering algorithm and of the self-learning algorithm; the fuzzy clustering algorithm has been widely applied in fault diagnosis of conventional mechanical systems, but is difficult to be applied for the fault diagnosis of railway vehicle running gears in the specific track-vehicle environment, due to the track irregularities. When combined with the self-learning algorithm, the new joint algorithm converts original featured values into clustering series as new judgement criteria by clustering samples in the same section, and then obtains the dynamic early warning threshold to realize the vibration monitoring and early warning of the railway vehicle running gear. A mechanical vibration test rig was built to verify the new joint algorithm. A monitoring and early warning software platform based on the joint algorithm was also developed to monitor and early warn the abnormal vibrations of the railway vehicle in real time. The experimental results show that the new method can efficiently identify the abnormal vibrations in the case of mechanical failure.
{"title":"Joint Self-learning and Fuzzy Clustering Algorithm for Early Warning Detection of Railway Running Gear Defects","authors":"Huiming Yao, C. Ulianov, Feng Liu","doi":"10.23919/IConAC.2018.8749115","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749115","url":null,"abstract":"The paper proposes a new feature pattern recognition method for early warning of defects of the railway vehicle running gear. Based on a large amount of historical data, a joint self-learning and fuzzy clustering algorithm was developed. The joint algorithm combines the advantages of the fuzzy clustering algorithm and of the self-learning algorithm; the fuzzy clustering algorithm has been widely applied in fault diagnosis of conventional mechanical systems, but is difficult to be applied for the fault diagnosis of railway vehicle running gears in the specific track-vehicle environment, due to the track irregularities. When combined with the self-learning algorithm, the new joint algorithm converts original featured values into clustering series as new judgement criteria by clustering samples in the same section, and then obtains the dynamic early warning threshold to realize the vibration monitoring and early warning of the railway vehicle running gear. A mechanical vibration test rig was built to verify the new joint algorithm. A monitoring and early warning software platform based on the joint algorithm was also developed to monitor and early warn the abnormal vibrations of the railway vehicle in real time. The experimental results show that the new method can efficiently identify the abnormal vibrations in the case of mechanical failure.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133789451","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8748976
Khalil Khan, Ikram Syed, Muhammad Sarwar Khan, M. Mazhar, Irfan Uddin, Nasir Ahmad
Face parts labeling is the process of assigning class labels to each face part. A face parts labeling method FPL which divides a given image into its constitutes parts is proposed in this paper. In most of the previously proposed methods this division is based on three or some time four classes. In the proposed work a given face image is divided into six classes (skin, hair, back, eyes, nose and mouth). A database FaceD consisting of 564 images is labeled with hand and make publically available. A supervised learning model is built through extraction of features from the training data. Testing phase is performed with two semantic segmentation methods i.e., pixel and super-pixel based segmentation. In pixel based segmentation class label is provided to each pixel individually. In super-pixel based method class label is assigned to super-pixels only – as a result same class label is given to all pixels inside a super-pixel. Pixel labeling accuracy reported with pixel and super-pixel based methods is 97.68% and 93.45% respectively.
{"title":"FPL-An End-to-End Face Parts Labeling Framework","authors":"Khalil Khan, Ikram Syed, Muhammad Sarwar Khan, M. Mazhar, Irfan Uddin, Nasir Ahmad","doi":"10.23919/IConAC.2018.8748976","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8748976","url":null,"abstract":"Face parts labeling is the process of assigning class labels to each face part. A face parts labeling method FPL which divides a given image into its constitutes parts is proposed in this paper. In most of the previously proposed methods this division is based on three or some time four classes. In the proposed work a given face image is divided into six classes (skin, hair, back, eyes, nose and mouth). A database FaceD consisting of 564 images is labeled with hand and make publically available. A supervised learning model is built through extraction of features from the training data. Testing phase is performed with two semantic segmentation methods i.e., pixel and super-pixel based segmentation. In pixel based segmentation class label is provided to each pixel individually. In super-pixel based method class label is assigned to super-pixels only – as a result same class label is given to all pixels inside a super-pixel. Pixel labeling accuracy reported with pixel and super-pixel based methods is 97.68% and 93.45% respectively.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131091254","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749009
B. Abbasi, Shahid Hussain, Shaista Bibi, M. A. Shah
In text categorization, the discriminative power of classifiers, dataset characteristics, and construction of the more representative feature set play an important role in classification decisions. Subsequently, in text categorization, filter based feature selection methods are used rather than wrapper and embedded methods. In terms of construction of an illustrative feature set, a number of global and local filter based feature selection methods are used with their respective pros and cons. The inclusion and exclusion of membership and non-membership features in a constructed feature set depends on the discriminative power of the feature selection method. Though, there are few studies which have reported the impact of non-membership features on the classification decision. However, to best of our knowledge, there is no detail study, which calibrates the effectiveness of the feature selection method in terms of inclusion of non-membership features to improve the classification decisions. Consequently, in this paper, we conduct an empirical study to investigate the effectiveness of four well-known filter based feature selection methods, namely IG, $chi 2$, RF, and DF. Subsequently, we perform a case study in the context of classification of the Gang-of-Four software design patterns. The results show that the balance consideration of membership and non-membership features has a positive impact on the performance of the classifier and classification decision can be improved. It has also been concluded that random forest is best among existing methods in considering an equal number of membership and non-membership features and the classifiers show better performance with this method as compare to others.
{"title":"Impact of Membership and Non-membership Features on Classification Decision: An Empirical Study for Appraisal of Feature Selection Methods","authors":"B. Abbasi, Shahid Hussain, Shaista Bibi, M. A. Shah","doi":"10.23919/IConAC.2018.8749009","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749009","url":null,"abstract":"In text categorization, the discriminative power of classifiers, dataset characteristics, and construction of the more representative feature set play an important role in classification decisions. Subsequently, in text categorization, filter based feature selection methods are used rather than wrapper and embedded methods. In terms of construction of an illustrative feature set, a number of global and local filter based feature selection methods are used with their respective pros and cons. The inclusion and exclusion of membership and non-membership features in a constructed feature set depends on the discriminative power of the feature selection method. Though, there are few studies which have reported the impact of non-membership features on the classification decision. However, to best of our knowledge, there is no detail study, which calibrates the effectiveness of the feature selection method in terms of inclusion of non-membership features to improve the classification decisions. Consequently, in this paper, we conduct an empirical study to investigate the effectiveness of four well-known filter based feature selection methods, namely IG, $chi 2$, RF, and DF. Subsequently, we perform a case study in the context of classification of the Gang-of-Four software design patterns. The results show that the balance consideration of membership and non-membership features has a positive impact on the performance of the classifier and classification decision can be improved. It has also been concluded that random forest is best among existing methods in considering an equal number of membership and non-membership features and the classifiers show better performance with this method as compare to others.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129791649","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749052
Weichen Yang, S. Miao, Yaowang Li, Binxin Yin, Junyao Liu
A source-load coordination scheduling strategy is proposed in this paper to reduce the system operation cost and wind power curtailment. Firstly, the scheduling model of the power system with wind power is established. To solve the scheduling problem, the binary particle swarm optimization (BPSO) algorithm is used to determine the ON/OFF states of generations; the continuous particle swarm optimization (CPSO) algorithm is used to deal with the economic load dispatch problem; and the constraints are properly handled by adjustment methods. Secondly, in order to maximize the wind power accommodation rate, the power system adopts the time-of-use price program, an optimization model of electricity price is established based on price elasticity matrix. The CPSO algorithm and parallel computing are used to optimize the time-of-use price schedules. According to the results of the case study, the demand response program plays an important role in reducing the peak-valley difference, wind power curtailment, and system operating cost. The proposed scheduling strategy and algorithm are proven to have a good optimization performance, calculation speed and stability.
{"title":"A Source-Load Coordination Scheduling Strategy Based on PSO algorithm and Parallel Computing","authors":"Weichen Yang, S. Miao, Yaowang Li, Binxin Yin, Junyao Liu","doi":"10.23919/IConAC.2018.8749052","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749052","url":null,"abstract":"A source-load coordination scheduling strategy is proposed in this paper to reduce the system operation cost and wind power curtailment. Firstly, the scheduling model of the power system with wind power is established. To solve the scheduling problem, the binary particle swarm optimization (BPSO) algorithm is used to determine the ON/OFF states of generations; the continuous particle swarm optimization (CPSO) algorithm is used to deal with the economic load dispatch problem; and the constraints are properly handled by adjustment methods. Secondly, in order to maximize the wind power accommodation rate, the power system adopts the time-of-use price program, an optimization model of electricity price is established based on price elasticity matrix. The CPSO algorithm and parallel computing are used to optimize the time-of-use price schedules. According to the results of the case study, the demand response program plays an important role in reducing the peak-valley difference, wind power curtailment, and system operating cost. The proposed scheduling strategy and algorithm are proven to have a good optimization performance, calculation speed and stability.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143029","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749036
Tao Liang, G. Yang, Yulan Dong, Siqi Qian, Yan Xu
Predicting the temperature variables of the wind turbine gearbox precisely including the axis temperature and the oil temperature can evaluate the gearbox status in real time effectively. Concerning the limitations of a single prediction model, this paper proposes a variable-weight combined model to predict gearbox temperature based on the theory of grey relational degree. Firstly, Principal Component Analysis (PCA) is used to reduce the dimension of the gearbox temperature related factors, and the time series is selected to analyze the internal structure of the gearbox temperature. Then, to analyze the gray correlation degree between the four single models and the actual temperature series, eliminate a certain dynamically model and to update the remaining models weights dynamically. Compared the variable-weight combined model with the equal-weight combined model and each single model, it is shown that the variable-weight combined prediction model has higher prediction accuracy, which is of great significance for further condition monitoring of the gearbox.
{"title":"Predicting Temperatures of Wind Turbine Gearbox By a Variable-Weight Combined Model","authors":"Tao Liang, G. Yang, Yulan Dong, Siqi Qian, Yan Xu","doi":"10.23919/IConAC.2018.8749036","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749036","url":null,"abstract":"Predicting the temperature variables of the wind turbine gearbox precisely including the axis temperature and the oil temperature can evaluate the gearbox status in real time effectively. Concerning the limitations of a single prediction model, this paper proposes a variable-weight combined model to predict gearbox temperature based on the theory of grey relational degree. Firstly, Principal Component Analysis (PCA) is used to reduce the dimension of the gearbox temperature related factors, and the time series is selected to analyze the internal structure of the gearbox temperature. Then, to analyze the gray correlation degree between the four single models and the actual temperature series, eliminate a certain dynamically model and to update the remaining models weights dynamically. Compared the variable-weight combined model with the equal-weight combined model and each single model, it is shown that the variable-weight combined prediction model has higher prediction accuracy, which is of great significance for further condition monitoring of the gearbox.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117019102","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8748978
Lei Han, Yisheng Zou, Guofu Ding, Menghao Zhu, Lei Jiang, S. Qin, H. Liang
This paper presents a new online Tool Condition Monitoring System (TCMS) based on Object Linking and Embedded (OLE) for Process Control (OPC) Automation Interface of Computer Numerical Control (CNC) system for shop floor applications. The developed TCMS is able to acquire, display and analyze the spindle power signals automatically from the Panel Control Unit (PCU) of a machine tool in real-time. Tool condition is remote monitored and automatically determined by using adaptive thresholds calculated through statistical method put forward. Experiments are carried out and verify the accuracy and utility of the developed system.
{"title":"Development of an Online Tool Condition Monitoring System for NC Machining Based on Spindle Power Signals","authors":"Lei Han, Yisheng Zou, Guofu Ding, Menghao Zhu, Lei Jiang, S. Qin, H. Liang","doi":"10.23919/IConAC.2018.8748978","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8748978","url":null,"abstract":"This paper presents a new online Tool Condition Monitoring System (TCMS) based on Object Linking and Embedded (OLE) for Process Control (OPC) Automation Interface of Computer Numerical Control (CNC) system for shop floor applications. The developed TCMS is able to acquire, display and analyze the spindle power signals automatically from the Panel Control Unit (PCU) of a machine tool in real-time. Tool condition is remote monitored and automatically determined by using adaptive thresholds calculated through statistical method put forward. Experiments are carried out and verify the accuracy and utility of the developed system.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196654","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749073
Ilyas Lahlouh, A. Elakkary, N. Sefiani
The poultry house model is governed through the nonlinear behavior of psychrometric mechanisms. In order to discern the dynamics of broiler house and to construct a convenient controller, we examine in this work, the problem of stabilizing temperature and humidity in poultry house model during the winter climate. The specific aim of this research is to analyze the application of the state feedback-Integrator controller to a multivariable system (MIMO). For this purpose, the designed control strategy is executed to adjust the required conditions of an optimal growth of the broilers. The proposed controller was implemented to maintain the relative humidity and temperature inside a poultry house under the cold conditions related to the Moroccan climate. The simulation results shows a good performance in terms of the state error and settling time.
{"title":"State Feedback controller in a closed poultry house system","authors":"Ilyas Lahlouh, A. Elakkary, N. Sefiani","doi":"10.23919/IConAC.2018.8749073","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749073","url":null,"abstract":"The poultry house model is governed through the nonlinear behavior of psychrometric mechanisms. In order to discern the dynamics of broiler house and to construct a convenient controller, we examine in this work, the problem of stabilizing temperature and humidity in poultry house model during the winter climate. The specific aim of this research is to analyze the application of the state feedback-Integrator controller to a multivariable system (MIMO). For this purpose, the designed control strategy is executed to adjust the required conditions of an optimal growth of the broilers. The proposed controller was implemented to maintain the relative humidity and temperature inside a poultry house under the cold conditions related to the Moroccan climate. The simulation results shows a good performance in terms of the state error and settling time.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123235126","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749041
Chao Tang, R. Stolkin, Chun-ling Hu, Huosheng Hu, Xiaofeng Wang, L. Zou
The selection of motion feature directly affects the recognition effect of human action recognition method. Single feature is often affected by human appearance, environment, camera settings and other factors, and its recognition effect is limited. This paper propose a novel action recognition method by using selective ensemble learning, which is a special paradigm of ensemble learning. Moreover, this paper presents a fast and efficient action description feature and a novel recognition algorithm. Robust discriminant mixed features are learnt from behavioral video frames as behavioral descriptors, The recogniton algorithm using selective ensemble learning can achieve fast classification. Experimental results show that the proposed method achieves ideal recognition results on the self-built indoor behavior data set and public data set.
{"title":"Selective Ensemble Learning based Human Action Recognition Using Fusing Visual Features","authors":"Chao Tang, R. Stolkin, Chun-ling Hu, Huosheng Hu, Xiaofeng Wang, L. Zou","doi":"10.23919/IConAC.2018.8749041","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749041","url":null,"abstract":"The selection of motion feature directly affects the recognition effect of human action recognition method. Single feature is often affected by human appearance, environment, camera settings and other factors, and its recognition effect is limited. This paper propose a novel action recognition method by using selective ensemble learning, which is a special paradigm of ensemble learning. Moreover, this paper presents a fast and efficient action description feature and a novel recognition algorithm. Robust discriminant mixed features are learnt from behavioral video frames as behavioral descriptors, The recogniton algorithm using selective ensemble learning can achieve fast classification. Experimental results show that the proposed method achieves ideal recognition results on the self-built indoor behavior data set and public data set.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117140567","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 : 2018-09-01DOI: 10.23919/IConAC.2018.8749067
Sinan A. Khwandah, J. Cosmas, Z. Zaharis, P. Lazaridis, I. Glover, Shadi M. Saleh
In this paper an adaptive power control for co-channel femtocells in presented. In order to avoid co-channel interference, the femtocells have to sense the presence of macro users in the surrounding environment. Interference is avoided through applying adaptive power control on the femtocell downlink. Also, the femtocells are equipped with a reporting mechanism and they are connected to a coordinator so that this provides direct connections between femtocells. In this scenario, the femtocell has the ability to report any interfering neighboring femtocell with the aid of a coordinator. Moreover, victim femto user at the cell edge can report the interfering femtocell to its serving femtocell or to the macrocell. Results show that the BLER requirements are fulfilled and interference could be reduced through applying the adaptive power control and the proposed reporting scenarios.
{"title":"Interference Management Scheme for Co-channel Femtocells","authors":"Sinan A. Khwandah, J. Cosmas, Z. Zaharis, P. Lazaridis, I. Glover, Shadi M. Saleh","doi":"10.23919/IConAC.2018.8749067","DOIUrl":"https://doi.org/10.23919/IConAC.2018.8749067","url":null,"abstract":"In this paper an adaptive power control for co-channel femtocells in presented. In order to avoid co-channel interference, the femtocells have to sense the presence of macro users in the surrounding environment. Interference is avoided through applying adaptive power control on the femtocell downlink. Also, the femtocells are equipped with a reporting mechanism and they are connected to a coordinator so that this provides direct connections between femtocells. In this scenario, the femtocell has the ability to report any interfering neighboring femtocell with the aid of a coordinator. Moreover, victim femto user at the cell edge can report the interfering femtocell to its serving femtocell or to the macrocell. Results show that the BLER requirements are fulfilled and interference could be reduced through applying the adaptive power control and the proposed reporting scenarios.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132489262","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 : 2018-09-01DOI: 10.23919/ICONAC.2018.8748972
R. Alzubi, N. Ramzan, Hadeel Alzoubi, Stamos Katsigiannis
Machine learning and data mining techniques have recently gained more popularity in the field of Medical diagnosis, especially for the analysis of the human genome. One of the most significant sources of human genome variation is Single Nucleotide Polymorphisms (SNPs), which have been associated with multiple human diseases. Several techniques have been developed for distinguishing between affected and healthy samples of SNP data. In this study, conditional mutual information maximisation (CMIM) has been employed in order to identify a subset of the most informative SNPs to be used in with various classifications algorithms for the detection of hypertension disease. Five classification algorithms have been evaluated, namely k-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), Naive Bayes (NB), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM), along with their combination into an unweighted majority voting ensemble classification scheme. The experimental evaluation of the proposed approach via supervised classification experiments showed that the ensemble approach using the SVM, 5-NN, and NB classifiers achieves the highest classification accuracy (93.21%) and F1 score (91.72%), demonstrating the suitability of the proposed approach for the detection of hypertension disease from SNPs data.
{"title":"SNPs-based Hypertension Disease Detection via Machine Learning Techniques","authors":"R. Alzubi, N. Ramzan, Hadeel Alzoubi, Stamos Katsigiannis","doi":"10.23919/ICONAC.2018.8748972","DOIUrl":"https://doi.org/10.23919/ICONAC.2018.8748972","url":null,"abstract":"Machine learning and data mining techniques have recently gained more popularity in the field of Medical diagnosis, especially for the analysis of the human genome. One of the most significant sources of human genome variation is Single Nucleotide Polymorphisms (SNPs), which have been associated with multiple human diseases. Several techniques have been developed for distinguishing between affected and healthy samples of SNP data. In this study, conditional mutual information maximisation (CMIM) has been employed in order to identify a subset of the most informative SNPs to be used in with various classifications algorithms for the detection of hypertension disease. Five classification algorithms have been evaluated, namely k-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), Naive Bayes (NB), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM), along with their combination into an unweighted majority voting ensemble classification scheme. The experimental evaluation of the proposed approach via supervised classification experiments showed that the ensemble approach using the SVM, 5-NN, and NB classifiers achieves the highest classification accuracy (93.21%) and F1 score (91.72%), demonstrating the suitability of the proposed approach for the detection of hypertension disease from SNPs data.","PeriodicalId":121030,"journal":{"name":"2018 24th International Conference on Automation and Computing (ICAC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126817393","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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