2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)最新文献

{"title":"Finding Longest (s, t)-paths of O-shaped Supergrid Graphs in Linear Time","authors":"Ruo-Wei Hung, Fatemeh Keshavarz-Kohjerdi, Yuh-Min Tseng, Guo-Hao Qiu","doi":"10.1109/ICAwST.2019.8923400","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923400","url":null,"abstract":"The longest path and Hamiltonian problems were known to be NP-complete. In spite of many applications of these problems, their complexities are still unknown for many classes of graphs, including supergrid graphs with holes and solid supergrid graphs. In this paper, we will study the complexity of the longest (s, t)-path problem on O-shaped supergrid graphs. The longest (s, t)-path is a simple path from s to t with the largest number of visited vertices. An O-shaped supergrid graph is a rectangular supergrid graph with one rectangular hollow. We will propose a linear-time algorithm to find the longest (s, t)-path of O-shaped supergrid graphs. The longest (s, t)-paths of O-shaped supergrid graphs can be used to compute the smallest stitching path of computerized embroidery machine and 3D printer when a hollow object is printed.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132335222","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}

{"title":"Diagnosis of Diabetes using an Extreme Learning Machine Algorithm based Model","authors":"M. Shanthi, R. Marimuthu, S. Shivapriya, R. Navaneethakrishnan","doi":"10.1109/ICAwST.2019.8923142","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923142","url":null,"abstract":"Diabetes is a disorder caused by an increase in blood glucose levels due to insulin secretion deficiency (type 1 diabetes) or impaired insulin activity (type 2 diabetes). More than 90% of people with this condition are diagnosed with type 2 diabetes. Diabetes is now the leading cause of blindness, end-stage renal failure, non-traumatic limb amputations, heart disease, and stroke. Due to the high prevalence of type 2 diabetes in recent years, the prognosis and early diagnosis of the disease have gained much importance. In this paper a study on the types of diabetes is made and a model is proposed and developed for diagnosis of type 2 diabetes using Extreme Learning Machine (ELM) method.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133845228","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}

{"title":"Alterations in brainwaves caused by different Music Genres","authors":"Shao-Kuo Tai, Yu-Jen Kuo","doi":"10.1109/ICAwST.2019.8923257","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923257","url":null,"abstract":"In recent years, many studies have shown that music affects brainwaves. However, music also has the power to stimulate strong emotions. If music is classified by emotion, will the music of different emotions have different effects on brain wavesƒ The focus of this study was to observe brainwaves as subjects listen to different emotional types of music to verify whether the music of different emotional categories has different effects on brain waves. We classify music by the circumplex model of emotion and select four pieces of music from the different subdivision in that model as our experimental music. Study adopt MindWave Mobile as the primary sensing component, which is a sensor for detecting brainwave and output the electroencephalogram (EEG). The collected brainwave data was tested and processed by the experimental module of this study and use T-Test to evaluate the effects of music on brainwaves. In the experimental results, we found that the arousal score of music in the circumplex model of emotion does have the positive relationship with the alpha wave, and for other brainwaves do not have adequate evidence to prove their connection.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133888218","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}

{"title":"A Comparative Study of the Stability of Filter based Feature Selection Algorithms","authors":"Rikta Sen, A. K. Mandal, Saptarsi Goswami, B. Chakraborty","doi":"10.1109/ICAwST.2019.8923245","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923245","url":null,"abstract":"Feature selection is an important step prior to classification stage of machine learning, pattern recognition and data mining problems for addressing the high dimensionality of the data. It removes irrelevant and redundant features which lead to simplify classification process and improve accuracy. Several feature selection algorithms have been proposed so far and quality of the selected feature subset varies from algorithm to algorithm. One of the measures for assessing the quality of a feature selection algorithm is its stability. Stability refers to the robustness of the selected feature set to small changes in the training set or set of various parameters of the algorithm. In this work, a comparative study of the stability of several well-known filter based feature selection algorithms, producing ranked feature sub set, has been done. Fifteen benchmark datasets from the UCI repository have been used for simulation experiments. Three types of stability measures, index-based, rank-based and weight based are used to evaluate the stability of feature selection algorithms. Simulation results demonstrate that for most of the datasets, JMD-based feature selection algorithm exhibits more stability irrespective of all types of stability measures. It is also observed that Relief shows the least stability.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"8 36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124640522","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}

{"title":"Re-identifying people from anonymous histories of their activities","authors":"H. Yoshiura","doi":"10.1109/ICAwST.2019.8923333","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923333","url":null,"abstract":"Privacy problems are major obstacles to collecting and using big data because, in many cases, big data reflects a person’s history of activities, such as moving around a city, buying goods, surfing the Web, and posting content on social media. Although anonymization is an effective technical measure for alleviating privacy concerns, we must be aware of two problems that could infringe privacy: re-identifying the people represented by the data despite anonymization and profiling people from the data. In this paper, we first survey reidentification techniques developed for various areas, clarify the relationship between re-identification and profiling, and mathematically model the re-identification problem. We then present methods for re-identifying social media accounts and location histories and present the results of evaluations demonstrating their effectiveness.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116080753","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}

{"title":"A New Steganography Method Based on Generative Adversarial Networks","authors":"Hiroshi Naito, Qiangfu Zhao","doi":"10.1109/ICAwST.2019.8923579","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923579","url":null,"abstract":"In this paper, we propose a new steganography method based on generative adversarial networks (GAN). In using steganography, the third party can extract the embedded secret easily by comparing the cover data and the hidden message if the cover data are publicly available (e.g. accessible in the internet). To avoid this problem, we need a unique cover datum for each piece of secret messages. As the cover data, we focus on digital images in this study. To make a lot of unique and natural looking images, we can use GAN. Actually, training of GAN results in two neural networks namely the generator and the discriminator. The generator makes virtual images, and the discriminator evaluates the naturalness of the virtual images. In the proposed method, we use both the generator and the discriminator to guarantee the naturalness of the cover data on the sender side, and to filter out stego data sent from malicious third party. In this experiments, we first confirmed the capability of the generator for producing unlimited number of cover data, and then investigated the possibility of naturalness checking using the discriminator. We believe that the proposed method can provide a better way for information hiding.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130260814","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}

{"title":"Route-Based Ship Classification","authors":"Shoichi Ichimura, Qiangfu Zhao","doi":"10.1109/ICAwST.2019.8923540","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923540","url":null,"abstract":"In recent years, the traffic volume on the sea has increased significantly. Compared with road traffic management, sea traffic management is very difficult due to many reasons. For safety sailing, automatic identification system (AIS) has been introduced. Using AIS signals, it is possible to understand the position, velocity, and other information of each sea-going ship, and thus can detect possible dangers and provide necessary rescue promptly. However, some ship owners may not set their AIS correctly, and thus the AIS signals may not be trustable. The purpose of this study is to propose a method to classify the true ship type using the AIS signal and provide a way to support traffic management. Specifically, we extract the \"signature characteristics\" of the ship from its AIS signal, and then classify the ship type using a machine learning model. Primary experimental results show that the average accuracy is about 87.3% if we use a multilayer perceptron. Better results are expected if we use more data.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124099416","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}

{"title":"A machine-learning approach for predicting success in smoking cessation intervention","authors":"Khishigsuren Davagdorj, Jong Seol Lee, K. Park, K. Ryu","doi":"10.1109/ICAwST.2019.8923252","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923252","url":null,"abstract":"Smoking is one of the significant avoidable risk factors for premature death. Most smokers make multiple quit attempts during their lifetime but smoking dependence is not easy and many people eventually failed in smoking quit. Therefore, predicting the likelihood of success in smoking cessation intervention is necessary for public health.In this paper, we analyzed the smoking cessation intervention dataset conducted from the Korea National Health and Nutrition Examination Survey (KNHANES) 2009 to 2017. Accordingly, the chi-square test used to filter relevant and significant features, thus the multivariate analysis was used with logistic regression. In essence, age, education, and frequent alcohol use are important predictors in smoking cessation success. Furthermore, the lowest level of subjective health status has increased the likelihood of unsuccessful smoking cessation.In terms of the class imbalance problem, we have employed an efficient Synthetic Minority Over-sampling Technique (SMOTE) in order to generate new synthetic records. In the current study, we compared the SMOTE regular and borderline-1 techniques with 3, 5 and 7 number of nearest neighbors, respectively. Subsequently, we evaluate the success prediction model of smoking intervention using Naïve Bayes (NB), logistic regression (LR), multilayer perceptron neural network (MLPNN), random forest (RF) and gradient boosting trees (GBT) classifiers, as well as classifier performance has evaluated by precision, recall and F-measure.Our result demonstrated that NB with SMOTE borderline-1 (K=5) outperformed the precision of 0.8701. Meanwhile, RF with SMOTE borderline-1 (K=5) performed of 0.8766 and F-score of 0.8476. On the contrary, However, LR presents the lowest F-score as SMOTE regular (K=3) of 0.6726 and borderline (K=3) of 0.6700 in experimental comparison result.In addition, a combination of statistical and machine learning techniques is supposed to be helpful tools in the decisions of smoking cessation intervention and public health domain.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114460258","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}

{"title":"Liver Cancer Prediction Using Synthetic Minority based on Probabilistic Distribution (SyMProD) Oversampling Technique","authors":"Intouch Kunakorntum, Woranich Hinthong, Sumet Amonyingchareon, P. Phunchongharn","doi":"10.1109/ICAwST.2019.8923122","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923122","url":null,"abstract":"Liver cancer is challenging to diagnose in general. Moreover, liver cancer prediction can be hindered by skewed data between majority and minority classes, and missing values. Many existing prediction models do not address these two limitations that can make classification results ignore minority instances (i.e., patients with liver cancer are not detected). In this paper, we present a liver cancer prediction model with a new oversampling technique called Synthetic Minority based on Probabilistic Distribution (SyMProD) to handle skewed patients’ data from Chulabhorn hospital. SyMProD removes noisy data based on z-score normalization value and adaptively selects referenced data using probability distribution from the ratio of minority and majority closeness factor. The proposed method oversamples minority instances from several minority nearest neighbors to cover the distribution. We employ Random Forest (RF) and Gradient Boosted Tree (GBT) to generate prediction models with stratified five-fold cross-validation. Results demonstrate that GBT with our proposed oversampling technique achieves a better result than other techniques. These results from our technique generate new instances in the minority distribution, avoid the majority region, remove the overgeneralization problem, and reduce possibilities of creating noise and overlapping classes. Our prediction model may help prompt high-risk patients to get a proper diagnosis and treatments in time.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132181248","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}

{"title":"Mobile EEG Based Drowsiness Detection using K-Nearest Neighbor","authors":"Prima Dewi Purnamasari, Pratiwi Yustiana, A. A. P. Ratna, D. Sudiana","doi":"10.1109/ICAwST.2019.8923161","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923161","url":null,"abstract":"In this research, a drowsiness detection system, named Drowsiver, was developed for a mobile electroencephalograph (EEG) and a mobile phone. The system is expected to minimize the causes of accidents caused by drowsy drivers. By using Electroencephalogram (EEG), the condition of drowsiness is detected by recording the electrical activity that occurs in the human brain and is represented as a frequency signal. The signal is transmitted to the Android mobile application via Bluetooth and will give an alarm notification if the drowsiness is detected. The brainwave from the mobile EEG is processed using Fast Fourier Transform (FFT) to extract its features. These features are classified using K-Nearest Neighbor (KNN) classifier. The system produces the best performance with the highest accuracy of 95.24% using the value of k=3 and four brain waves as features, namely Delta, Theta, Alpha, and Beta waves.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131535007","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}