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8th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)

作者： Aing, Lee Kondo, Toshiaki Nilkhamhang, Itthisek Bunnun, Pished Kaneko, Hirohiko Thammasat Univ Sch Informat & Commun Technol Sirindhorn Int Inst Technol Pathum Thani Thailand Natl Sci & Technol Dev Agcy Natl Elect & Comp Technol Ctr Pathum Thani Thailand Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781509048090

Eye state recognition is still challenging in the field of computer vision. Many researchers have reported that their methods can work well with frontal face views, but not with variations of head poses. Some have described that their methods deal effectively with head pose problems, but the systems are complex to implement and consume a lot of processing time. In this paper, a novel method of eye state recognition is presented in order to overcome the head pose and time complexity problems. Moreover, the system which can be simply divided into two main steps is easy to implement. The idea is to use machine learning algorithm to localize eye regions, and then classify eye states according to the summation of many Hamming distances. The Hamming distance is computed between two observed eye image intensities which stay next to each other along the vertical axis. Since eye opening always scores higher than eye closing, this enables eye states to be distinguish correctly. There are two databases are used for evaluating the proposed method, and the experimental results show that more than 98% of accuracy can be achieved, with time complexity of 14 milliseconds, for a single 384 by 286 resolution image.

关键词： eye state recognition machine learning algorithm eye regions Hamming distance eye image intensities

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Delay Analysis of Dynamic Bandwidth Allocation for Triple-Play-Services in EPON 5

Delay Analysis of Dynamic Bandwidth Allocation for Triple-Pl...

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5th International Conference on Cyber and IT Service Management (CITSM)

作者： Liem, Andrew Tanny Sandag, Green Arther Hwang, I-Shyan Nikoukar, AliAkbar Univ Klabat Dept Comp Sci Sulawesi Utara Indonesia Yuan Ze Univ Dept Comp Sci & Engn Taoyuan Taiwan Univ Yasuj Dept Math Coll Sci Yasuj Iran

ISBN: (纸本)9781538627372

Today, triple play services may be regarded as required services in telecommunication sector. In the access network, Ethernet Passive Optical Network (EPON) is considered as one of the best solutions to provision triple-play-services. In EPON, the Dynamic Bandwidth Allocation (DBA) algorithm plays a key role to meet the Quality-of-Service (QoS) requirements. Therefore, in this paper, we conduct the delay analysis of DBA so that it can provide a better understanding for the network operators when designing and implementing a DBA algorithm. We use machine learning algorithm such as Random Forest, Decision Tree, kNN, and Naives Bayes and R-programming to build a prediction model and find the important factors affecting the packet delay in EPON system. We run extensive simulation scenarios to build appropriate dataset for training set and testing set. We found that the cycle time selection has a significant impact on the EF delay, AF delay and BE grant. We also found that there was a strong relationship between the EF delay and AF GRANT. Lastly, we conduct performance evaluation to justify what we found. Simulation results show that cycle time is indeed has significant effect on system performances.

关键词： Triple-Play-Services EPON DBA QoS machine learning algorithm R-Programming

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Data mining approach to identify disease cohorts from primary care electronic medical records: A case of diabetes mellitus

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Open Bioinformatics Journal 2017年 10卷 16-27页

作者： Owusu Adjah, Ebenezer S. Montvida, Olga Agbeve, Julius Paul, Sanjoy K. QIMR Berghofer Medical Research Institute Brisbane Australia Faculty of Medicine The University of Queensland Brisbane Australia School of Biomedical Sciences Institute of Health and Biomedical Innovation Faculty of Health Queensland University of Technology Brisbane Australia Melbourne EpiCentre University of Melbourne and Melbourne Health Melbourne Australia

Background: Identification of diseased patients from primary care based electronic medical records (EMRs) has methodological challenges that may impact epidemiologic inferences. Objective: To compare deterministic clinically guided selection algorithms with probabilistic machine learning (ML) methodologies for their ability to identify patients with type 2 diabetes mellitus (T2DM) from large population based EMRs from nationally representative primary care database. Methods: Four cohorts of patients with T2DM were defined by deterministic approach based on disease codes. The database was mined for a set of best predictors of T2DM and the performance of six ML algorithms were compared based on cross-validated true positive rate, true negative rate, and area under receiver operating characteristic curve. Results: In the database of 11,018,025 research suitable individuals, 379 657 (3.4%) were coded to have T2DM. Logistic Regression classifier was selected as best ML algorithm and resulted in a cohort of 383,330 patients with potential T2DM. Eighty-three percent (83%) of this cohort had a T2DM code, and 16% of the patients with T2DM code were not included in this ML cohort. Of those in the ML cohort without disease code, 52% had at least one measure of elevated glucose level and 22% had received at least one prescription for antidiabetic medication. Conclusion: Deterministic cohort selection based on disease coding potentially introduces significant mis-classification problem. ML techniques allow testing for potential disease predictors, and under meaningful data input, are able to identify diseased cohorts in a holistic way. © 2017, Bentham Science Publishers B.V. All Rights reserved.

关键词： Cohort identification Diabetes Electronic medical records machine learning algorithm Primary care database Type 2 diabetes

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Diabetes Prediction Using Ensemble Perceptron algorithm 9

Diabetes Prediction Using Ensemble Perceptron Algorithm

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9th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Mirshahvalad, Roxana Zanjani, Nastaran Asadi Eastern Mediterranean Univ Dept Comp Engn Gazimagusa Cyprus Ecole Polytech Fed Lausanne Dept Elect Engn Lausanne Switzerland

ISBN: (纸本)9781509050017

Today, people's new way of life leads their eating habits towards fast-foods and ready-to-use products more than before. These foods contain large amounts of sugar and fat, which increase the number of people at risk of diabetes. Many people are required to get diabetes diagnosis by various blood tests regularly. These tests bring significant amounts of cost and take facilities and time when it comes to a large number of people. machine learning algorithms can be used as computer aided systems to predict if a person is highly probable to have diabetes or not, in order to reduce huge number of people who require to take diagnosis blood tests, to save time and money. In this study, we proposed a learning algorithm which ensemble boosting algorithm with perceptron algorithm to improve performance of perceptron algorithm in prediction of undiagnosed patients. Proposed method is tested on three different publicly available datasets and compared with performance of perceptron algorithm. The results show that proposed algorithm outperform perceptron algorithm on average AUC basis.

关键词： ensemble learning perceptron algorithm machine learning algorithm boosting algorithm diabetes prediction

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Waah: Infants Cry Classification of Physiological State Based on Audio Features 5

Waah: Infants Cry Classification of Physiological State Base...

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International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT)

作者： Rodriguez, Ramon L. Caluya, Susan S. Natl Univ Coll Comp Studies Manila Philippines

ISBN: (纸本)9780769561639

Crying is one of the means by which they express their emotional, psychological and physiological states. This paper investigates if audio features specifically the Mel Frequency Cepstral Coefficient (MFCC) & Linear Prediction Cepstral Coefficient (LPCC) are enough to classify the infants' physiological states such as hunger, pain and discomfort. To determine these physiological states, we recorded the cry of some infants who were immunized. The data for hunger and discomfort were recorded in the infants' house. The results showed that the audio features can classify an infant's physiological state. We used three classification algorithms, Decision Tree (J48), Neural Network and Support Vector machine in developing the infant physiological model. To evaluate the performance of the infant physiological state model, Precision, Recall and F-measure were used as performance metrics. We found out that Decision Tree and Multilayer Perceptron performed better for the given dataset with correctly classified instances ranging from 87.64% to 90.80 with an overall kappa statistic ranging from 0.47-0.64.

关键词： audio features infants state machine learning algorithm audio signal infants cry

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Diagnosis of Alzheimer's Disease using machine learning

Diagnosis of Alzheimer's Disease using Machine Learning

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International Conference on Computing Communication Control and Automation

作者： Priyanka Lodha Ajay Talele Kishori Degaonkar Department of EnTC Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781538652589;9781538652572

machine learning is being widely used in various medical fields. Advances in medical technologies have given access to better data for identifying symptoms of various diseases in early stages. Alzheimer's disease is chronic condition that leads to degeneration of brain cells leading at memory enervation. Patients with cognitive mental problems such as confusion and forgetfulness, also other symptoms including behavioral and psychological problems are further suggested having CT, MRI, PET, EEG, and other neuroimaging techniques. The aim of this paper is making use of machine learning algorithms to process this data obtained by neuroimaging technologies for detection of Alzheimer's in its primitive stage.

关键词： Linear regression machine learning algorithm Alzheimer's disease symptoms Random forest Gradient boosting algorithm Neural network

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Mapping Plastic-Mulched Farmland with C-Band Full Polarization SAR Remote Sensing Data

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REMOTE SENSING 2017年第12期9卷 1264-1264页

作者： Hasituya Chen, Zhongxin Li, Fei Hongmei Minist Educ Key Lab Grassland Resources Beijing Peoples R China Minist Agr Key Lab Forage Cultivat Proc & High Efficient Uti Beijing Peoples R China Inner Mongolia Agr Univ Coll Grassland Resources & Environm 29 Erdos Dongjie Saihan Dist 010011 Hohhot Peoples R China Minist Agr Key Lab Agr Remote Sensing Beijing Peoples R China Chinese Acad Agr Sci AGRIRS Inst Agr Resources & Reg Planning 12 Zhongguancun Nan Dajie Beijing 100081 Peoples R China

Plastic mulching is an important technology in agricultural production both in China and the rest of the world. In spite of its benefit of increasing crop yields, the booming expansion of the plastic mulching area has been changing the landscape patterns and affecting the environment. Accurate and effective mapping of Plastic-Mulched Farmland (PMF) can provide useful information for leveraging its advantages and disadvantages. However, mapping the PMF with remote sensing is still challenging owing to its varying spectral characteristics with the crop growth and geographic spatial division. In this paper, we investigated the potential of Radarsat-2 data for mapping PMF. We obtained the backscattering intensity of different polarizations and multiple polarimetric decomposition descriptors. These remotely-sensed information was used as input features for Random Forest (RF) and Support Vector machine (SVM) classifiers. The results indicated that the features from Radarsat-2 data have great potential for mapping PMF. The overall accuracies of PMF mapping with Radarsat-2 data were close to 75%. Although the classification accuracy with the back-scattering intensity information alone was relatively lower owing to the inherent speckle noise in SAR data, it has been improved significantly by introducing the polarimetric decomposition descriptors. The accuracy was nearly 75%. In addition, the features derived from the Entropy/Anisotropy/Alpha (H/A/Alpha) polarimetric decomposition, such as Alpha, entropy, and so on, made a greater contribution to PMF mapping than the Freeman decomposition, Krogager decomposition and the Yamaguchi4 decomposition. The performances of different classifiers were also compared. In this study, the RF classifier performed better than the SVM classifier. However, it is expected that the classification accuracy of PMF with SAR remote sensing data can be improved by combining SAR remote sensing data with optical remote sensing data.

关键词： plastic-mulched farmland mapping Radarsat-2 data backscattering intensity polarimetric decomposition machine learning algorithm

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BEARING FAULT COMPONENT IDENTIFICATION USING INFORMATION GAIN AND machine learning algorithmS

BEARING FAULT COMPONENT IDENTIFICATION USING INFORMATION GAI...

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Conference on Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure

作者： Vinay, Vakharia Kumar, Gupta Vijay Kumar, Kankar Pavan PDPM Indian Inst Informat Technol Mech Engn Discipline Design & Mfg Jabalpur Jabalpur 482005 India

ISBN: (纸本)9781628415407

In the present study an attempt has been made to identify various bearing faults using machine learning algorithm. Vibration signals obtained from faults in inner race, outer race, rolling element and combined faults are considered. Raw vibration signal cannot be used directly since vibration signals are masked by noise. To overcome this difficulty combined time frequency domain method such as wavelet transform is used. Further wavelet selection criteria based on minimum permutation entropy is employed to select most appropriate base wavelet. Statistical features from selected wavelet coefficients are calculated to form feature vector. To reduce size of feature vector information gain attribute selection method is employed. Modified feature set is fed in to machine learning algorithm such as random forest and self-organizing map for getting maximize fault identification efficiency. Results obtained revealed that attribute selection method shows improvement in fault identification accuracy of bearing components.

关键词： Bearing faults Wavelet transform Minimum permutation entropy Information gain machine learning algorithm

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Preserved white matter microstructure in young patients with anorexia nervosa?

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HUMAN BRAIN MAPPING 2016年第11期37卷 4069-4083页

作者： Pfuhl, Gerit King, Joseph A. Geisler, Daniel Roschinski, Benjamin Ritschel, Franziska Seidel, Maria Bernardoni, Fabio Mueller, Dirk K. White, Tonya Roessner, Veit Ehrlich, Stefan Tech Univ Dresden Dept Child & Adolescent Psychiat Eating Disorders Res & Treatment Ctr Fac Med Dresden Germany UiT Arctic Univ Norway Dept Psychol Trondheim Norway Norwegian Univ Sci & Technol Dept Psychol Trondheim Norway Tech Univ Dresden Fac Med Div Psychol & Social Med & Dev Neurosci Dresden Germany Tech Univ Dresden Dept Psychiat Dresden Germany Tech Univ Dresden Neuroimaging Ctr Dresden Germany Erasmus Univ Med Ctr Dept Child & Adolescent Psychiat Rotterdam Netherlands Erasmus Univ Med Ctr Dept Radiol Rotterdam Netherlands Massachusetts Gen Hosp MGH MIT HMS Martinos Ctr Biomed Imaging Charlestown MA USA Harvard Med Sch Massachusetts Gen Hosp Dept Psychiat Boston MA USA

econstruction A massive but reversible reduction of cortical thickness and subcortical gray matter (GM) volumes in Anorexia Nervosa (AN) has been recently reported. However, the literature on alterations in white matter (WM) volume and microstructure changes in both acutely underweight AN (acAN) and after recovery (recAN) is sparse and results are inconclusive. Here, T1-weighted and diffusion-weighted MRI data in a sizable sample of young and medication-free acAN (n=35), recAN (n=32), and age-matched female healthy controls (HC, n=62) were obtained. For analysis, a well-validated global probabilistic tractography r algorithm including rigorous motion correction implemented in FreeSurfer: TRACULA (TRActs Constrained by UnderLying Anatomy) were used. Additionally, a clustering algorithm and a multivariate pattern classification technique to WM metrics to predict group membership were applied. No group differences in either WM volume or WM microstructure were detected with standard analysis procedures either in acAN or recAN relative to HC after controlling for the number of performed statistical tests. These findings were not affected by age, IQ, or psychiatric symptoms. While cluster analysis was unsuccessful at discriminating between groups, multivariate pattern classification showed some ability to separate acAN from HC (but not recAN from HC). However, these results were not compatible with a straightforward hypothesis of impaired WM microstructure. The current findings suggest that WM integrity is largely preserved in non-chronic AN. This finding stands in contrast to findings in GM, but may help to explain the relatively intact cognitive performance of young patients with AN and provide the basis for the fast recovery of GM structures. Hum Brain Mapp 37:4069-4083, 2016. (c) 2016 Wiley Periodicals, Inc.

关键词： diffusion tensor imaging tractography fractional anisotropy machine learning algorithm anorexia nervosa

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Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device

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SENSORS 2017年第9期17卷 2067-2067页

作者： Jeon, Hyoseon Lee, Woongwoo Park, Hyeyoung Lee, Hong Ji Kim, Sang Kyong Kim, Han Byul Jeon, Beomseok Park, Kwang Suk Seoul Natl Univ Interdisciplinary Program Bioengn Seoul 03080 South Korea Seoul Natl Univ Hosp Dept Neurol Seoul 03080 South Korea Seoul Natl Univ Hosp Movement Disorder Ctr Seoul 03080 South Korea Seoul Natl Univ Coll Med Dept Biomed Engn Seoul 03080 South Korea

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson's Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson's disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.

关键词： tremor UPDRS automatic scoring Parkinson's disease wearable device machine learning algorithm

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