The inherent intangible nature, complexity, context-specific interpretations of emotions make it difficult to quantify and model affective space. Dimensional theory is one of the effective methods to describe and model emotions. Despite recent advances in affective computing, modeling continuous affective space remains a challenge. Here, we present a computational framework to study the role of functional areas of brain and band frequencies in modeling 4-D continuous affective space (Valence, Arousal, Like and Dominance). In particular, we used Electroencephalogram (EEG) recordings and adopted a recursive feature elimination (RFE) approach to select band frequencies and electrode locations (functional areas) that are most relevant for predicting affective space. Empirical analyses on DEAP dataset [1] reveals that only a small number of locations (7-12) and certain band frequencies carry most discriminative information. Using the selected features, we modeled 4-D affective space using Support Vector Regression (SVR). Regression analysis show that Root Mean Square Error (RMSE) for Valence, Arousal, Dominance, Like are 1.40, 1.23, 1.24 and, 1.24, respectively. Besides SVR, the performance of feature fusion and ensemble classifiers were also compared to determine the robust model against technical noise and individual variations. It was observed that the prediction accuracy of the final model is up to 37% better than human judgment evaluated on same data set. Spillover effect of our approach may include design of task-specific (i.e., emotion, memory capacity) EEG headset with a minimal number of electrodes.
{"title":"Robust Modeling of Continuous 4-D Affective Space from EEG Recording","authors":"Rakib Al-Fahad, M. Yeasin","doi":"10.1109/ICMLA.2016.0188","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0188","url":null,"abstract":"The inherent intangible nature, complexity, context-specific interpretations of emotions make it difficult to quantify and model affective space. Dimensional theory is one of the effective methods to describe and model emotions. Despite recent advances in affective computing, modeling continuous affective space remains a challenge. Here, we present a computational framework to study the role of functional areas of brain and band frequencies in modeling 4-D continuous affective space (Valence, Arousal, Like and Dominance). In particular, we used Electroencephalogram (EEG) recordings and adopted a recursive feature elimination (RFE) approach to select band frequencies and electrode locations (functional areas) that are most relevant for predicting affective space. Empirical analyses on DEAP dataset [1] reveals that only a small number of locations (7-12) and certain band frequencies carry most discriminative information. Using the selected features, we modeled 4-D affective space using Support Vector Regression (SVR). Regression analysis show that Root Mean Square Error (RMSE) for Valence, Arousal, Dominance, Like are 1.40, 1.23, 1.24 and, 1.24, respectively. Besides SVR, the performance of feature fusion and ensemble classifiers were also compared to determine the robust model against technical noise and individual variations. It was observed that the prediction accuracy of the final model is up to 37% better than human judgment evaluated on same data set. Spillover effect of our approach may include design of task-specific (i.e., emotion, memory capacity) EEG headset with a minimal number of electrodes.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122033613","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}
Wind power prediction is of great importance in the utilization of renewable wind power. A lot of research has been done attempting to improve the accuracy of wind power predictions and has achieved desirable performance. However, there is no complete performance evaluation of machine learning methods. This paper presents an extensive empirical study of machine learning methods for wind power predictions. Nine various models are considered in this study which also includes the application and evaluation of deep learning techniques. The experimental data consists of seven datasets based on wind farms in Ontario, Canada. The results indicate that SVM, followed by ANN, has the best overall performance, and that k-NN method is suitable for longer ahead predictions. Despite the findings that deep learning fails to give improvement in basic predictions, it shows the potential for more abstract prediction tasks, such as spatial correlation predictions.
{"title":"An Empirical Study on Machine Learning Models for Wind Power Predictions","authors":"Yiqian Liu, Huajie Zhang","doi":"10.1109/ICMLA.2016.0135","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0135","url":null,"abstract":"Wind power prediction is of great importance in the utilization of renewable wind power. A lot of research has been done attempting to improve the accuracy of wind power predictions and has achieved desirable performance. However, there is no complete performance evaluation of machine learning methods. This paper presents an extensive empirical study of machine learning methods for wind power predictions. Nine various models are considered in this study which also includes the application and evaluation of deep learning techniques. The experimental data consists of seven datasets based on wind farms in Ontario, Canada. The results indicate that SVM, followed by ANN, has the best overall performance, and that k-NN method is suitable for longer ahead predictions. Despite the findings that deep learning fails to give improvement in basic predictions, it shows the potential for more abstract prediction tasks, such as spatial correlation predictions.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123742975","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}
Rajendra Rana Bhat, R. Trevizan, Rahul Sengupta, Xiaolin Li, A. Bretas
Fraud detection in electricity consumption is a major challenge for power distribution companies. While many pattern recognition techniques have been applied to identify electricity theft, they often require extensive handcrafted feature engineering. Instead, through deep layers of transformation, nonlinearity, and abstraction, Deep Learning (DL) automatically extracts key features from data. In this paper, we design spatial and temporal deep learning solutions to identify nontechnical power losses (NTL), including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Stacked Autoencoder. These models are evaluated in a modified IEEE 123-bus test feeder. For the same tests, we also conduct comparison experiments using three conventional machine learning approaches: Random Forest, Decision Trees and shallow Neural Networks. Experimental results demonstrate that the spatiotemporal deep learning approaches outperform conventional machine learning approaches.
{"title":"Identifying Nontechnical Power Loss via Spatial and Temporal Deep Learning","authors":"Rajendra Rana Bhat, R. Trevizan, Rahul Sengupta, Xiaolin Li, A. Bretas","doi":"10.1109/ICMLA.2016.0052","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0052","url":null,"abstract":"Fraud detection in electricity consumption is a major challenge for power distribution companies. While many pattern recognition techniques have been applied to identify electricity theft, they often require extensive handcrafted feature engineering. Instead, through deep layers of transformation, nonlinearity, and abstraction, Deep Learning (DL) automatically extracts key features from data. In this paper, we design spatial and temporal deep learning solutions to identify nontechnical power losses (NTL), including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Stacked Autoencoder. These models are evaluated in a modified IEEE 123-bus test feeder. For the same tests, we also conduct comparison experiments using three conventional machine learning approaches: Random Forest, Decision Trees and shallow Neural Networks. Experimental results demonstrate that the spatiotemporal deep learning approaches outperform conventional machine learning approaches.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124865226","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}
Bonny Banerjee, Masoumeh Heidari Kapourchali, S. Najnin, L. L. Mendel, Sungmin Lee, Chhayakanta Patro, Monique Pousson
Does a hearing-impaired individual's speech reflect his hearing loss, and if it does, can the nature of hearing loss be inferred from his speech? To investigate these questions, at least four hours of speech data were recorded from each of 37 adult individuals, both male and female, belonging to four classes: 7 normal, and 30 severely-to-profoundly hearing impaired with high, medium or low speech intelligibility. Acoustic kernels were learned for each individual by capturing the distribution of his speech data points represented as 20 ms duration windows. These kernels were evaluated using a set of neurophysiological metrics, namely, distribution of characteristic frequencies, equal loudness contour, bandwidth and Q10 value of tuning curve. Our experimental results reveal that a hearing-impaired individual's speech does reflect his hearing loss provided his loss of hearing has considerably affected the intelligibility of his speech. For such individuals, the lack of tuning in any frequency range can be inferred from his learned speech kernels.
{"title":"Inferring Hearing Loss from Learned Speech Kernels","authors":"Bonny Banerjee, Masoumeh Heidari Kapourchali, S. Najnin, L. L. Mendel, Sungmin Lee, Chhayakanta Patro, Monique Pousson","doi":"10.1109/ICMLA.2016.0014","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0014","url":null,"abstract":"Does a hearing-impaired individual's speech reflect his hearing loss, and if it does, can the nature of hearing loss be inferred from his speech? To investigate these questions, at least four hours of speech data were recorded from each of 37 adult individuals, both male and female, belonging to four classes: 7 normal, and 30 severely-to-profoundly hearing impaired with high, medium or low speech intelligibility. Acoustic kernels were learned for each individual by capturing the distribution of his speech data points represented as 20 ms duration windows. These kernels were evaluated using a set of neurophysiological metrics, namely, distribution of characteristic frequencies, equal loudness contour, bandwidth and Q10 value of tuning curve. Our experimental results reveal that a hearing-impaired individual's speech does reflect his hearing loss provided his loss of hearing has considerably affected the intelligibility of his speech. For such individuals, the lack of tuning in any frequency range can be inferred from his learned speech kernels.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927270","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}
Xiang Zhang, Jonathan Tong, Nishant Vishwamitra, E. Whittaker, Joseph P. Mazer, Robin M. Kowalski, Hongxin Hu, Feng Luo, J. Macbeth, Edward C. Dillon
Cyberbullying can have a deep and long lasting impact on its victims, who are often adolescents. Accurately detecting cyberbullying helps prevent it. However, the noise and errors in social media posts and messages make detecting cyberbullying very challenging. In this paper, we propose a novel pronunciation based convolutional neural network (PCNN) to address this challenge. Upon observing that the pronunciation of misspelled words in informal online conversations is often unchanged, we used the phoneme codes of the text as the features for a convolutional neural network. This procedure corrects spelling errors that did not alter the pronunciation, thereby alleviating the problem of noise and bullying data sparsity. To overcome class imbalance, a common problem in cyberbullying datasets, we implement three techniques that include threshold-moving, cost function adjusting, and a hybrid solution in our model. We evaluate the performance of our models using two cyberbullying datasets collected from Twitter and Formspring.me. The results of our experiment show that PCNN can achieve improved recall and precision compared to baseline convolutional neural networks.
{"title":"Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network","authors":"Xiang Zhang, Jonathan Tong, Nishant Vishwamitra, E. Whittaker, Joseph P. Mazer, Robin M. Kowalski, Hongxin Hu, Feng Luo, J. Macbeth, Edward C. Dillon","doi":"10.1109/ICMLA.2016.0132","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0132","url":null,"abstract":"Cyberbullying can have a deep and long lasting impact on its victims, who are often adolescents. Accurately detecting cyberbullying helps prevent it. However, the noise and errors in social media posts and messages make detecting cyberbullying very challenging. In this paper, we propose a novel pronunciation based convolutional neural network (PCNN) to address this challenge. Upon observing that the pronunciation of misspelled words in informal online conversations is often unchanged, we used the phoneme codes of the text as the features for a convolutional neural network. This procedure corrects spelling errors that did not alter the pronunciation, thereby alleviating the problem of noise and bullying data sparsity. To overcome class imbalance, a common problem in cyberbullying datasets, we implement three techniques that include threshold-moving, cost function adjusting, and a hybrid solution in our model. We evaluate the performance of our models using two cyberbullying datasets collected from Twitter and Formspring.me. The results of our experiment show that PCNN can achieve improved recall and precision compared to baseline convolutional neural networks.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129704547","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}
We propose a dynamic Bayesian network approach to forecast the short-term passenger flows of the urban rail network of Paris. This approach can deal with the incompleteness of the data caused by failures or lack of collection systems. The structure of the model is based on the causal relationships between the adjacent flows and is designed to take into account the transport service. To reduce the number of arcs and find the maximum likelihood estimate of the parameters, we perform the structural expectation-maximization (EM) algorithm. Then short-term forecasting is conducted by inference, using the bootstrap filter. An experiment is carried out on an entire metro line, using ticket validation, count and transport service data. Overall, the forecasting results outperform historical average and last observation carried forward (LOCF). They illustrate the potential of the approach, as well as the key role of the transport service.
{"title":"Short-Term Urban Rail Passenger Flow Forecasting: A Dynamic Bayesian Network Approach","authors":"J. Roos, S. Bonnevay, G. Gavin","doi":"10.1109/ICMLA.2016.0187","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0187","url":null,"abstract":"We propose a dynamic Bayesian network approach to forecast the short-term passenger flows of the urban rail network of Paris. This approach can deal with the incompleteness of the data caused by failures or lack of collection systems. The structure of the model is based on the causal relationships between the adjacent flows and is designed to take into account the transport service. To reduce the number of arcs and find the maximum likelihood estimate of the parameters, we perform the structural expectation-maximization (EM) algorithm. Then short-term forecasting is conducted by inference, using the bootstrap filter. An experiment is carried out on an entire metro line, using ticket validation, count and transport service data. Overall, the forecasting results outperform historical average and last observation carried forward (LOCF). They illustrate the potential of the approach, as well as the key role of the transport service.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128990961","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}
Christian Bodenstein, Markus Goetz, Annika Jansen, H. Scholz, M. Riedel
In this paper, we propose an instrumentation and computer vision pipeline that allows automatic object detection on images taken from multiple experimental set ups. We demonstrate the approach by autonomously counting intoxicated flies in the FLORIDA assay. The assay measures the effect of ethanol exposure onto the ability of a vinegar fly Drosophila melanogaster to right itself. The analysis consists of a three-step approach. First, obtaining an image of a large set of individual experiments, second, identify areas containing a single experiment, and third, discover the searched objects within the experiment. For the analysis we facilitate well-known computer vision and machine learning algorithms - namely color segmentation, threshold imaging and DBSCAN. The automation of the experiment enables an unprecedented reproducibility and consistency, while significantly decreasing the manual labor.
{"title":"Automatic Object Detection Using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay","authors":"Christian Bodenstein, Markus Goetz, Annika Jansen, H. Scholz, M. Riedel","doi":"10.1109/ICMLA.2016.0133","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0133","url":null,"abstract":"In this paper, we propose an instrumentation and computer vision pipeline that allows automatic object detection on images taken from multiple experimental set ups. We demonstrate the approach by autonomously counting intoxicated flies in the FLORIDA assay. The assay measures the effect of ethanol exposure onto the ability of a vinegar fly Drosophila melanogaster to right itself. The analysis consists of a three-step approach. First, obtaining an image of a large set of individual experiments, second, identify areas containing a single experiment, and third, discover the searched objects within the experiment. For the analysis we facilitate well-known computer vision and machine learning algorithms - namely color segmentation, threshold imaging and DBSCAN. The automation of the experiment enables an unprecedented reproducibility and consistency, while significantly decreasing the manual labor.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127603293","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}
In this paper, we propose an efficient solution for the privacy-preserving of a bipartite ranking algorithm. The bipartite ranking problem can be considered as finding a function that ranks positive instances (in a dataset) higher than the negative ones. However, one common concern for all the existing schemes is the privacy of individuals in the dataset. That is, one (e.g., a researcher) needs to access the records of all individuals in the dataset in order to run the algorithm. This privacy concern puts limitations on the use of sensitive personal data for such analysis. The RIMARC (Ranking Instances by Maximizing Area under the ROC Curve) algorithm solves the bipartite ranking problem by learning a model to rank instances. As part of the model, it learns weights for each feature by analyzing the area under receiver operating characteristic (ROC) curve. RIMARC algorithm is shown to be more accurate and efficient than its counterparts. Thus, we use this algorithm as a building-block and provide a privacy-preserving version of the RIMARC algorithm using homomorphic encryption and secure multi-party computation. Our proposed algorithm lets a data owner outsource the storage and processing of its encrypted dataset to a semi-trusted cloud. Then, a researcher can get the results of his/her queries (to learn the ranking function) on the dataset by interacting with the cloud. During this process, neither the researcher nor the cloud learns any information about the raw dataset. We prove the security of the proposed algorithm and show its efficiency via experiments on real data.
{"title":"A Privacy-Preserving Solution for the Bipartite Ranking Problem","authors":"N. Faramarzi, Erman Ayday, H. Altay Güvenir","doi":"10.1109/ICMLA.2016.0067","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0067","url":null,"abstract":"In this paper, we propose an efficient solution for the privacy-preserving of a bipartite ranking algorithm. The bipartite ranking problem can be considered as finding a function that ranks positive instances (in a dataset) higher than the negative ones. However, one common concern for all the existing schemes is the privacy of individuals in the dataset. That is, one (e.g., a researcher) needs to access the records of all individuals in the dataset in order to run the algorithm. This privacy concern puts limitations on the use of sensitive personal data for such analysis. The RIMARC (Ranking Instances by Maximizing Area under the ROC Curve) algorithm solves the bipartite ranking problem by learning a model to rank instances. As part of the model, it learns weights for each feature by analyzing the area under receiver operating characteristic (ROC) curve. RIMARC algorithm is shown to be more accurate and efficient than its counterparts. Thus, we use this algorithm as a building-block and provide a privacy-preserving version of the RIMARC algorithm using homomorphic encryption and secure multi-party computation. Our proposed algorithm lets a data owner outsource the storage and processing of its encrypted dataset to a semi-trusted cloud. Then, a researcher can get the results of his/her queries (to learn the ranking function) on the dataset by interacting with the cloud. During this process, neither the researcher nor the cloud learns any information about the raw dataset. We prove the security of the proposed algorithm and show its efficiency via experiments on real data.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127901713","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}
Clustering vertices in graphs or in sequences of graphs has important applications in network science, bioinformatics, and other areas. Most research to date has focused on static graphs or sequences where the number of vertices does not change. We propose a new algorithm that successfully partitions the vertices of a graph sequence into smooth clusters, even when the number of vertices is allowed to vary over time. Our approach uses spectral clustering and relies on applying the k partition problem to a graph constructed from the input graph sequence. Several experiments demonstrate the performance of our method and its advantages over existing methods.
{"title":"Detecting Smooth Cluster Changes in Evolving Graphs","authors":"Sohei Okui, Kaho Osamura, Akihiro Inokuchi","doi":"10.1109/ICMLA.2016.0066","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0066","url":null,"abstract":"Clustering vertices in graphs or in sequences of graphs has important applications in network science, bioinformatics, and other areas. Most research to date has focused on static graphs or sequences where the number of vertices does not change. We propose a new algorithm that successfully partitions the vertices of a graph sequence into smooth clusters, even when the number of vertices is allowed to vary over time. Our approach uses spectral clustering and relies on applying the k partition problem to a graph constructed from the input graph sequence. Several experiments demonstrate the performance of our method and its advantages over existing methods.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121312206","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}
We propose a novel demographic group prediction mechanism for smart device users based upon the recognition of user gestures. The core idea of our proposed approach is to utilize data from a variety of the internal environmental sensors in the device to predict useful demographics information. In order to achieve this objective, an application with several intuitive user interfaces was implemented and used to capture user data. The results presented here are based upon the data from fifty users. These captured data are integrated or fused, pre-processed, analyzed, and used as training data for a supervised machine learning predictive approach. The data reduction methods are based upon principal component analysis (PCA) and linear discriminant analysis (LDA). PCA/LDA were implemented to reduce the data feature dimensions and to improve the k-nearest neighbors (KNN) supervised classification predictions. The results of our experiment indicate that high accuracy is achieved from this method. To the best of our knowledge, this is the first research that uses user recognition gestures to predict multiple demographic groups.
{"title":"Demographic Group Prediction Based on Smart Device User Recognition Gestures","authors":"Adel R. Alharbi, M. Thornton","doi":"10.1109/ICMLA.2016.0025","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0025","url":null,"abstract":"We propose a novel demographic group prediction mechanism for smart device users based upon the recognition of user gestures. The core idea of our proposed approach is to utilize data from a variety of the internal environmental sensors in the device to predict useful demographics information. In order to achieve this objective, an application with several intuitive user interfaces was implemented and used to capture user data. The results presented here are based upon the data from fifty users. These captured data are integrated or fused, pre-processed, analyzed, and used as training data for a supervised machine learning predictive approach. The data reduction methods are based upon principal component analysis (PCA) and linear discriminant analysis (LDA). PCA/LDA were implemented to reduce the data feature dimensions and to improve the k-nearest neighbors (KNN) supervised classification predictions. The results of our experiment indicate that high accuracy is achieved from this method. To the best of our knowledge, this is the first research that uses user recognition gestures to predict multiple demographic groups.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123241604","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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